NYTimes:Blogs: Popular classification:: NYTBlogs3_feat_PubDate
bdanalytics
Date: (Sat) Nov 21, 2015
Introduction:
Data: Source: Training: https://www.kaggle.com/c/15-071x-the-analytics-edge-competition-spring-2015/download/NYTimesBlogTrain.csv
New: https://www.kaggle.com/c/15-071x-the-analytics-edge-competition-spring-2015/download/NYTimesBlogTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glbFeatsText
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(6) # # of cores on machine - 2
suppressPackageStartupMessages(require(caret))
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
glb_trnng_url <- "https://www.kaggle.com/c/15-071x-the-analytics-edge-competition-spring-2015/download/NYTimesBlogTrain.csv"
glb_newdt_url <- "https://www.kaggle.com/c/15-071x-the-analytics-edge-competition-spring-2015/download/NYTimesBlogTest.csv"
glbInpMerge <- NULL #: default
# list(fnames = c("<fname1>", "<fname2>")) # files will be concatenated
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- FALSE # select from c(FALSE, TRUE)
glb_split_newdata_method <- NULL # select from c(NULL, "condition", "sample", "copy")
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glbObsDropCondition <- NULL # : default
# "<condition>" # use | & ; NOT || &&
#parse(text=glbObsDropCondition)
#subset(glbObsAll, .grpid %in% c(31))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Popular"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Pplr.fctr" # glb_rsp_var_raw # or "Pplr.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
# glb_map_rsp_raw_to_var(tst <- c(NA, 0, 1))
# glb_map_rsp_raw_to_var(tst <- c(NA, 0, 2.99, 280.50, 1000.00))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
as.numeric(var) - 1
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# NewsDesk = the New York Times desk that produced the story (Business, Culture, Foreign, etc.)
# SectionName = the section the article appeared in (Opinion, Arts, Technology, etc.)
# SubsectionName = the subsection the article appeared in (Education, Small Business, Room for Debate, etc.)
# Headline = the title of the article
# Snippet = a small portion of the article text
# Abstract = a summary of the blog article, written by the New York Times
# WordCount = the number of words in the article
# PubDate = the publication date, in the format "Year-Month-Day Hour:Minute:Second"
# UniqueID = a unique identifier for each article
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category & work each one in
, "NewsDesk", "SectionName", "SubsectionName"
, "WordCount", "PubDate"
# Feature Engineering done with prior features
, "Headline", "Snippet", "Abstract"
)
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["carrier.fctr"]] <- "cellular.fctr"
# currently does not handle more than 1 column; consider concatenating multiple columns
glb_id_var <- "UniqueID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "NDSSName.my.fctr" # choose from c(NULL : default, "<category>")
glb_drop_vars <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
# character
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
glbFeatsDerive[["NDSSName.my"]] <- list(
mapfn = function(NewsDesk, SectionName, SubsectionName) {
descriptor <-
gsub(" ", "", paste(NewsDesk, SectionName, SubsectionName, sep = "#"))
return(gsub("[a|e|i|o|u]", "", plyr::revalue(descriptor, c(NULL
, "#BusinessDay#Dealbook" = "Business#BusinessDay#Dealbook"
, "#BusinessDay#SmallBusiness" = "Business#BusinessDay#SmallBusiness"
, "#Crosswords/Games#" = "Business#Crosswords/Games#"
, "#Open#" = "Business#Technology#"
, "#Technology#" = "Business#Technology#"
, "Business##" = "Business#Technology#"
, "#Arts#" = "Culture#Arts#"
, "Foreign##" = "Foreign#World#"
, "#World#AsiaPacific" = "Foreign#World#AsiaPacific"
, "#N.Y./Region#" = "Metro#N.Y./Region#"
, "#Opinion#" = "OpEd#Opinion#"
, "OpEd##" = "OpEd#Opinion#"
, "#Health#" = "Science#Health#"
, "Science##" = "Science#Health#"
, "Styles#Health#" = "Science#Health#"
, "Styles##" = "Styles##Fashion"
, "Styles#Style#Fashion&Style" = "Styles##Fashion"
, "#Travel#" = "Travel#Travel#"
, "Magazine#Magazine#" = "myOther"
, "National##" = "myOther"
, "National#U.S.#Politics" = "myOther"
, "Sports##" = "myOther"
, "Sports#Sports#" = "myOther"
, "#U.S.#" = "myOther"
))))
}
, args = c("NewsDesk", "SectionName", "SubsectionName"))
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# return(mod_raw) }
#print(mod_raw <- grep(""", glbObsAll[, txt_var], value = TRUE))
#print(mod_raw <- glbObsAll[c(88,187,280,1040,1098), txt_var])
#print(mod_raw <- glbObsAll[sel_obs(list(descr.my.contains="\\bdoes( +)not\\b")), glbFeatsText])
#print(mod_raw <- glbObsAll[sel_obs(list(descr.my.contains="\\bipad [[:digit:]]\\b")), glbFeatsText][01:10])
#print(mod_raw <- glbObsAll[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][11:20])
#print(mod_raw <- glbObsAll[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][21:30])
#print(mod_raw <- glbObsAll[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][31:40])
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txt_var]][, subset(glb_post_stop_words_terms_df_lst[[txt_var]], term %in% c("conditionminimal"))$pos] > 0), "description"]
# numeric
# Create feature based on record position/id in data
# glbFeatsDerive[["dummy.my"]] <- list(
# mapfn = function(UniqueID) { return(UniqueID) }
# , args = c("UniqueID"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
glbFeatsDerive[["WordCount.log1p"]] <- list(
mapfn = function(WordCount) { return(log1p(WordCount)) }
, args = c("WordCount"))
glbFeatsDerive[["WordCount.root2"]] <- list(
mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
, args = c("WordCount"))
glbFeatsDerive[["WordCount.nexp"]] <- list(
mapfn = function(WordCount) { return(exp(-WordCount)) }
, args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
glb_derive_vars <- names(glbFeatsDerive)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
glbFeatsDateTime[["PubDate"]] <-
c(format = "%Y-%m-%d %H:%M:%S", timezone = "America/New_York", impute.na = TRUE,
last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsText <- NULL # c("<txt_var>") # NULL #
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "NYTBlogs3_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (!is.null(glbFeatsText)) {
require(tm)
glb_txt_stop_words[["<txt_var>"]] <- sort(c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glb_nzv_freqCut); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^2", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txt_var]][, 6] > 0, glbFeatsText]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq <= 2)$term), collapse = ",")
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txt_var]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txt_var]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txt_var]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^m", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txt_var]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (nzv.freqRatio >= glb_nzv_freqCut) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txt_var]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txt_var)]
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txt_var]][grep("^moder", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txt_var]][grep("^came$", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txt_var]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txt_var]][grep("condit", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^con", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glb_id_var, "productline", txt_var)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glb_id_var, glbFeatsCategory, txt_var)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txt_var]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^c", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glb_txt_corpus_lst[[txt_var]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glb_txt_synonyms <- list()
#glb_txt_synonyms[["<txt_var>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
# options include: "weightTf", "myweightTflog1p", "myweightTfsqrt", "weightTfIdf", "weightBM25"
glb_txt_terms_control <- list(weighting = "weightTfIdf" # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- glbFeatsText
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- glbFeatsText
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txt_var>>]] <- list()
#glbFeatsTextPatterns[[<txt_var>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txt_var1> = 0.988, <txt_var2> = 0.970, <txt_var3> = 0.970)
glbFctrMaxUniqVals <- 21 # default: 20
glb_impute_na_data <- TRUE # FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- glb_rsp_var # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glb_nzv_freqCut <- 19 # 19 : caret default
glb_nzv_uniqueCut <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glb_id_var>>
# )
glbObsTrnOutliers <- list()
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glb_id_var))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glb_id_var])
#print(subset(model_diags_df, is.na(.dffits))[, glb_id_var])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glb_id_var])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glb_id_var, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glb_id_var, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glb_id_var, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glb_id_var, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
) else
glbMdlMethods <- c(NULL
# non-deterministic
, "rf"
# Unknown
, "gbm", "rpart"
)
glb_mdl_family_lst <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glb_mdl_family_lst[["RFE.X"]] <- c("glmnet", "glm") # non-NULL list is mandatory
glb_mdl_family_lst[["All.X"]] <- "glmnet" # non-NULL list is mandatory
#glb_mdl_family_lst[["Best.Interact"]] <- "glmnet" # non-NULL list is mandatory
# Check if interaction features make RFE better
# glb_mdl_family_lst[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glb_tune_models_df <- data.frame()
# Experiment specific code to avoid caret crash
glmnet_tune_models_df <- rbind(data.frame()
,data.frame(method = "glmnet", parameter = "alpha",
vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(method = "glmnet", parameter = "lambda",
vals = "9.342e-02")
)
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indep_vars, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glb_sel_mdl_id <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c(glb_id_var, glbFeatsCategory, glb_rsp_var
# List critical cols excl. glb_id_var, glbFeatsCategory & glb_rsp_var
)
# Output specs
glbOutDataVizFname <- "NYTBlogs3_obsall.csv" # choose from c(NULL, "NYTBlogs3_obsall.csv")
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
glb_out_vars_lst <- list()
# glb_id_var will be the first output column, by default
glb_out_vars_lst[["Probability1"]] <-
"%<d-% mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$prob"
# glb_out_vars_lst[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glb_out_vars_lst[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glb_out_pfx <- "NYTBlogs3_feat_PubDate_"
glb_save_envir <- FALSE # or TRUE
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 9.172 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/NYTimesBlogTrain.csv..."
## [1] "dimensions of data in ./data/NYTimesBlogTrain.csv: 6,532 rows x 10 cols"
## NewsDesk SectionName SubsectionName
## 1 Business Crosswords/Games
## 2 Culture Arts
## 3 Business Business Day Dealbook
## 4 Business Business Day Dealbook
## 5 Science Health
## 6 Science Health
## Headline
## 1 More School Daze
## 2 New 96-Page Murakami Work Coming in December
## 3 Public Pension Funds Stay Mum on Corporate Expats
## 4 Boot Camp for Bankers
## 5 Of Little Help to Older Knees
## 6 A Benefit of Legal Marijuana
## Snippet
## 1 A puzzle from Ethan Cooper that reminds me that a bill is due.
## 2 The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His Years of Pilgrimage.
## 3 Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little about the strategy, which could hurt the nations tax base.
## 4 As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service members ideal customers.
## 5 Middle-aged and older patients are unlikely to benefit in the long term from surgery to repair tears in the meniscus, pads of cartilage in the knee, a new review of studies has found.
## 6 A new study has found evidence that legal access to marijuana is associated with fewer opioid overdose deaths, but researchers said their findings should not be used as the basis for the wide adoption of legalized cannabis.
## Abstract
## 1 A puzzle from Ethan Cooper that reminds me that a bill is due.
## 2 The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His Years of Pilgrimage.
## 3 Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little about the strategy, which could hurt the nations tax base.
## 4 As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service members ideal customers.
## 5 Middle-aged and older patients are unlikely to benefit in the long term from surgery to repair tears in the meniscus, pads of cartilage in the knee, a new review of studies has found.
## 6 A new study has found evidence that legal access to marijuana is associated with fewer opioid overdose deaths, but researchers said their findings should not be used as the basis for the wide adoption of legalized cannabis.
## WordCount PubDate Popular UniqueID
## 1 508 2014-09-01 22:00:09 1 1
## 2 285 2014-09-01 21:14:07 0 2
## 3 1211 2014-09-01 21:05:36 0 3
## 4 1405 2014-09-01 20:43:34 1 4
## 5 181 2014-09-01 18:58:51 1 5
## 6 245 2014-09-01 18:52:22 1 6
## NewsDesk SectionName SubsectionName
## 226 Styles
## 995
## 3327
## 4753 Multimedia
## 4802 Business Crosswords/Games
## 6463 TStyle
## Headline
## 226 For Tavi Gevinson, Fashion Takes a Back Seat, for Now
## 995 Reconsidering What to Call an Extremist Group
## 3327 Clinton's Diagnosis of What's Wrong With Politics
## 4753 'Off Color' and on Target About Race in America
## 4802 Daniel Finkel's Circle-Toss Game
## 6463 Entering the Void
## Snippet
## 226 Tavi Gevinson, the teenage fashion star turned Broadway actress, wont be much of a player at New York Fashion Week this season.
## 995 Editors have decided to adjust how The Times refer to an Islamic extremist group that controls territory in Syria and Iraq.
## 3327 Hillary Rodham Clinton continued to laugh off questions about her presidential aspirations on Tuesday, but she did shed some light on what she thinks is wrong in Washington.
## 4753 Off Color, a New York Times video series, looks at how artists of color are making sharp social commentary about race in America through comedy and performance.
## 4802 By math educator Daniel Finkel, a puzzle thats childs play. Can you figure it out?
## 6463 The Spanish artist Miquel Barcel closely examines the basic materials of life in response to Edward Hirsch questioning his own belief in a higher power.
## Abstract
## 226 Tavi Gevinson, the teenage fashion star turned Broadway actress, wont be much of a player at New York Fashion Week this season.
## 995 Editors have decided to adjust how The Times refer to an Islamic extremist group that controls territory in Syria and Iraq.
## 3327 Hillary Rodham Clinton continued to laugh off questions about her presidential aspirations on Tuesday, but she did shed some light on what she thinks is wrong in Washington.
## 4753 Off Color, a New York Times video series, looks at how artists of color are making sharp social commentary about race in America through comedy and performance.
## 4802 By math educator Daniel Finkel, a puzzle thats childs play. Can you figure it out?
## 6463 The Spanish artist Miquel Barcel closely examines the basic materials of life in response to Edward Hirsch questioning his own belief in a higher power.
## WordCount PubDate Popular UniqueID
## 226 459 2014-09-04 16:55:57 0 226
## 995 301 2014-09-15 16:05:13 0 995
## 3327 236 2014-10-14 14:45:51 0 3327
## 4753 393 2014-11-02 05:00:13 0 4753
## 4802 1628 2014-11-03 12:00:04 1 4802
## 6463 264 2014-11-27 12:00:09 0 6463
## NewsDesk SectionName SubsectionName
## 6527 Foreign
## 6528 Opinion Room For Debate
## 6529 Foreign
## 6530 TStyle
## 6531 Multimedia
## 6532 Business
## Headline
## 6527 1914: Russians Dominate in East Poland
## 6528 Finding a Secretary of Defense
## 6529 1889: Metropolitan Opera House Reopens in New York
## 6530 The Daily Gift: Picasso Plates for Creative Dining
## 6531 Racing From New York to Barcelona
## 6532 Math Anxiety: Why Hollywood Makes Robots of Alan Turing and Other Geniuses
## Snippet
## 6527 From the International Herald Tribune archives: Russians dominate in East Poland in 1914.
## 6528 If Chuck Hagel isn't the right Pentagon chief to respond to an onslaught of global crises, who is?
## 6529 From the International Herald Tribune archives: The Metropolitan Opera House reopens in New York in 1889.
## 6530 Each day until Christmas, the editors of T share a new holiday gift idea.
## 6531 A sailboat race from New York to Barcelona was the setting for a thrilling and sometimes terrifying video about this challenging sport.
## 6532 The visionary who stares at formulas written on walls or mirrors or better yet, thin air has become a Hollywood trope. So has the depiction of the genius who cant connect with real people.
## Abstract
## 6527 From the International Herald Tribune archives: Russians dominate in East Poland in 1914.
## 6528 If Chuck Hagel isn't the right Pentagon chief to respond to an onslaught of global crises, who is?
## 6529 From the International Herald Tribune archives: The Metropolitan Opera House reopens in New York in 1889.
## 6530 Each day until Christmas, the editors of T share a new holiday gift idea.
## 6531 A sailboat race from New York to Barcelona was the setting for a thrilling and sometimes terrifying video about this challenging sport.
## 6532 The visionary who stares at formulas written on walls or mirrors or better yet, thin air has become a Hollywood trope. So has the depiction of the genius who cant connect with real people.
## WordCount PubDate Popular UniqueID
## 6527 176 2014-11-30 13:48:40 0 6527
## 6528 1597 2014-11-30 13:27:23 0 6528
## 6529 214 2014-11-30 09:44:57 0 6529
## 6530 61 2014-11-30 09:00:43 0 6530
## 6531 441 2014-11-30 09:00:22 0 6531
## 6532 921 2014-11-30 07:00:40 0 6532
## 'data.frame': 6532 obs. of 10 variables:
## $ NewsDesk : chr "Business" "Culture" "Business" "Business" ...
## $ SectionName : chr "Crosswords/Games" "Arts" "Business Day" "Business Day" ...
## $ SubsectionName: chr "" "" "Dealbook" "Dealbook" ...
## $ Headline : chr "More School Daze" "New 96-Page Murakami Work Coming in December" "Public Pension Funds Stay Mum on Corporate Expats" "Boot Camp for Bankers" ...
## $ Snippet : chr "A puzzle from Ethan Cooper that reminds me that a bill is due." "The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His"| __truncated__ "Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little"| __truncated__ "As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service "| __truncated__ ...
## $ Abstract : chr "A puzzle from Ethan Cooper that reminds me that a bill is due." "The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His"| __truncated__ "Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little"| __truncated__ "As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service "| __truncated__ ...
## $ WordCount : int 508 285 1211 1405 181 245 258 893 1077 188 ...
## $ PubDate : chr "2014-09-01 22:00:09" "2014-09-01 21:14:07" "2014-09-01 21:05:36" "2014-09-01 20:43:34" ...
## $ Popular : int 1 0 0 1 1 1 0 1 1 0 ...
## $ UniqueID : int 1 2 3 4 5 6 7 8 9 10 ...
## - attr(*, "comment")= chr "glbObsTrn"
## NULL## [1] "Reading file ./data/NYTimesBlogTest.csv..."
## [1] "dimensions of data in ./data/NYTimesBlogTest.csv: 1,870 rows x 9 cols"
## NewsDesk SectionName SubsectionName
## 1 Culture
## 2 Culture Arts
## 3 Business Crosswords/Games
## 4 Business Business Day Dealbook
## 5 Science Health
## 6 Science Health
## Headline
## 1 'Birdman' Tops the Gothams
## 2 'Sleepy Hollow' Recap: A Not-So-Shocking Death
## 3 Drinking Buddy For Falstaff
## 4 Encouraging Public Service, Through Wall Street's 'Revolving Door'
## 5 Therapy Prevents Repeat Suicide Attempts
## 6 Hoping for a Good Death
## Snippet
## 1 The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner.
## 2 In the fall season finale, a question of where the series has many places to go.
## 3 In which Timothy Polin reveals his potty mouth.
## 4 The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than on good public policy.
## 5 Short-term psychotherapy may be an effective way to prevent repeated suicide attempts.
## 6 What I hadnt considered before my fathers heart attack was the precise meaning of not wanting to live hooked up to machines.
## Abstract
## 1 The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner.
## 2 In the fall season finale, a question of where the series has many places to go.
## 3 In which Timothy Polin reveals his potty mouth.
## 4 The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than on good public policy.
## 5 Short-term psychotherapy may be an effective way to prevent repeated suicide attempts.
## 6 What I hadnt considered before my fathers heart attack was the precise meaning of not wanting to live hooked up to machines.
## WordCount PubDate UniqueID
## 1 111 2014-12-01 22:45:24 6533
## 2 558 2014-12-01 22:01:34 6534
## 3 788 2014-12-01 22:00:26 6535
## 4 915 2014-12-01 21:04:13 6536
## 5 213 2014-12-01 19:13:20 6537
## 6 938 2014-12-01 19:05:12 6538
## NewsDesk SectionName SubsectionName
## 3 Business Crosswords/Games
## 334 OpEd Opinion
## 725 TStyle
## 732 Business Business Day Dealbook
## 752 Business Business Day Dealbook
## 864
## Headline
## 3 Drinking Buddy For Falstaff
## 334 Facts & Figures: America’s Unique Take on Maternity Leave
## 725 Ansel Elgort Buttons Up in Brioni
## 732 A Shake-Up as the Financial World Infiltrates Philanthropy
## 752 Coupang, a South Korean E-Commerce Site, Raises $300 Million
## 864 Today in Politics
## Snippet
## 3 In which Timothy Polin reveals his potty mouth.
## 334 In the U.S., paid parental leave is more of a perk than a guarantee.
## 725 The actor brought a tinge of youthfulness to the classic Italian houses retro-tailored look.
## 732 Donor-advised funds help investors get deductions for charitable donations in one year, but society doesnt get the benefit of the money right away.
## 752 The latest financing round underscores Coupangs maturity and its ambitions to one day be a publicly traded company.
## 864 The 113th Congress is concluding with partisan brinksmanship and one last mad scramble for votes to pass a $1.1 trillion spending package.
## Abstract
## 3 In which Timothy Polin reveals his potty mouth.
## 334 In the U.S., paid parental leave is more of a perk than a guarantee.
## 725 The actor brought a tinge of youthfulness to the classic Italian houses retro-tailored look.
## 732 Donor-advised funds help investors get deductions for charitable donations in one year, but society doesnt get the benefit of the money right away.
## 752 The latest financing round underscores Coupangs maturity and its ambitions to one day be a publicly traded company.
## 864 The 113th Congress is concluding with partisan brinksmanship and one last mad scramble for votes to pass a $1.1 trillion spending package.
## WordCount PubDate UniqueID
## 3 788 2014-12-01 22:00:26 6535
## 334 160 2014-12-04 11:45:20 6866
## 725 89 2014-12-10 12:30:47 7257
## 732 1172 2014-12-10 12:00:38 7264
## 752 353 2014-12-10 08:30:41 7284
## 864 1544 2014-12-11 07:09:25 7396
## NewsDesk SectionName SubsectionName
## 1865
## 1866 Business Technology
## 1867 Metro N.Y. / Region
## 1868 Multimedia
## 1869 Foreign World Asia Pacific
## 1870 Science Health
## Headline
## 1865 Today in Politics
## 1866 Uber Suspends Operations in Spain
## 1867 New York Today: The Year in News
## 1868 New Year, Old Memories, in Times Square
## 1869 Hong Kong Police Criticized After 14-Year-Old's Detention
## 1870 The Super-Short Workout and Other Fitness Trends
## Snippet
## 1865 House Republicans are ending the year on a defensive note over Representative Steve Scalises 2002 speech to a white supremacist group.
## 1866 In a first in the growing pushback against Ubers global expansion, a judges ruling barred telecommunications operators and banks from supporting the companys services.
## 1867 Wednesday: The most read stories of 2014, teeth-chattering cold, and its New Years Eve.
## 1868 What happens when you combine Burning Man, Independence Day fireworks, the last day of school and a full-contact Black Friday sale-a-bration? New Years Eve in Times Square.
## 1869 The authorities have been accused of trying to intimidate young pro-democracy protesters and their families after a 14-year-old girl was detained on suspicion of drawing flowers in chalk near government headquarters and sent to a juvenile home.
## 1870 The big story in exercise science this year was the super-short workout, although many other fitness-related themes emerged in 2014.
## Abstract
## 1865 House Republicans are ending the year on a defensive note over Representative Steve Scalises 2002 speech to a white supremacist group.
## 1866 In a first in the growing pushback against Ubers global expansion, a judges ruling barred telecommunications operators and banks from supporting the companys services.
## 1867 Wednesday: The most read stories of 2014, teeth-chattering cold, and its New Years Eve.
## 1868 What happens when you combine Burning Man, Independence Day fireworks, the last day of school and a full-contact Black Friday sale-a-bration? New Years Eve in Times Square.
## 1869 The authorities have been accused of trying to intimidate young pro-democracy protesters and their families after a 14-year-old girl was detained on suspicion of drawing flowers in chalk near government headquarters and sent to a juvenile home.
## 1870 The big story in exercise science this year was the super-short workout, although many other fitness-related themes emerged in 2014.
## WordCount PubDate UniqueID
## 1865 1616 2014-12-31 07:03:46 8397
## 1866 292 2014-12-31 06:09:32 8398
## 1867 1010 2014-12-31 06:06:58 8399
## 1868 387 2014-12-31 05:00:19 8400
## 1869 717 2014-12-31 04:16:29 8401
## 1870 818 2014-12-31 00:01:10 8402
## 'data.frame': 1870 obs. of 9 variables:
## $ NewsDesk : chr "Culture" "Culture" "Business" "Business" ...
## $ SectionName : chr "" "Arts" "Crosswords/Games" "Business Day" ...
## $ SubsectionName: chr "" "" "" "Dealbook" ...
## $ Headline : chr "'Birdman' Tops the Gothams" "'Sleepy Hollow' Recap: A Not-So-Shocking Death" "Drinking Buddy For Falstaff" "Encouraging Public Service, Through Wall Street's 'Revolving Door'" ...
## $ Snippet : chr "The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner." "In the fall season finale, a question of where the series has many places to go." "In which Timothy Polin reveals his potty mouth." "The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than "| __truncated__ ...
## $ Abstract : chr "The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner." "In the fall season finale, a question of where the series has many places to go." "In which Timothy Polin reveals his potty mouth." "The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than "| __truncated__ ...
## $ WordCount : int 111 558 788 915 213 938 1336 2644 752 99 ...
## $ PubDate : chr "2014-12-01 22:45:24" "2014-12-01 22:01:34" "2014-12-01 22:00:26" "2014-12-01 21:04:13" ...
## $ UniqueID : int 6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 ...
## - attr(*, "comment")= chr "glbObsNew"
## NULL## [1] "Partition stats:"## Loading required package: sqldf
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## 2 Test 1870## Loading required package: dplyr
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## 2 Test 1870## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 9.172 20.291 11.119
## 2 inspect.data 2 0 0 20.292 NA NAStep 2.0: inspect data
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## Popular.0 Popular.1 Popular.NA
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## 1870
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## WordCount Popular
## 109 5439
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## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0## Popular Pplr.fctr .n
## 1 0 N 5439
## 2 NA <NA> 1870
## 3 1 Y 1093## Warning: Removed 1 rows containing missing values (position_stack).
## Pplr.fctr.N Pplr.fctr.Y Pplr.fctr.NA
## Test NA NA 1870
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## Test NA NA 1
## Train 0.8326699 0.1673301 NA## Loading required package: plyr
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## 2 inspect.data 2 0 0 20.292 23.326 3.034
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## 1870 1870
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## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 23.327 24.356 1.029
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## 1 24.509 24.519 0.01
## 2 24.520 NA NA## NewsDesk SectionName SubsectionName Headline
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## 3 24.538 NA NA
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## PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p
## 2 4 8
## PubDate.last16.log1p PubDate.last32.log1p
## 16 32## Loading required package: mice
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## mice 2.25 2015-11-09
## [1] "Summary before imputation: "
## NewsDesk SectionName SubsectionName
## Length:8402 Length:8402 Length:8402
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Headline Snippet Abstract
## Length:8402 Length:8402 Length:8402
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
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##
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## WordCount PubDate UniqueID .src
## Min. : 0.0 Length:8402 Min. : 1 Length:8402
## 1st Qu.: 188.0 Class :character 1st Qu.:2101 Class :character
## Median : 377.0 Mode :character Median :4202 Mode :character
## Mean : 528.8 Mean :4202
## 3rd Qu.: 735.0 3rd Qu.:6302
## Max. :10912.0 Max. :8402
##
## .rnorm NDSSName.my WordCount.log1p WordCount.root2
## Min. :-3.89398 Length:8402 Min. :0.000 Min. : 0.00
## 1st Qu.:-0.65896 Class :character 1st Qu.:5.242 1st Qu.: 13.71
## Median : 0.02058 Mode :character Median :5.935 Median : 19.42
## Mean : 0.01549 Mean :5.751 Mean : 20.60
## 3rd Qu.: 0.67764 3rd Qu.:6.601 3rd Qu.: 27.11
## Max. : 3.74468 Max. :9.298 Max. :104.46
##
## WordCount.nexp NDSSName.my.fctr PubDate.year.fctr
## Min. :0.00000 ## :1626 2014:8402
## 1st Qu.:0.00000 Bsnss#BsnssDy#Dlbk:1256
## Median :0.00000 Cltr#Arts# : 849
## Mean :0.01318 TStyl## : 829
## 3rd Qu.:0.00000 OpEd#Opnn# : 690
## Max. :1.00000 Bsnss#Tchnlgy# : 453
## (Other) :2699
## PubDate.month.fctr PubDate.date.fctr PubDate.juliandate
## 09:2341 (0.97,7]:1981 Min. :244.0
## 10:2382 (7,13] :1757 1st Qu.:270.0
## 11:1809 (13,19] :1808 Median :297.0
## 12:1870 (19,25] :1650 Mean :300.1
## (25,31] :1206 3rd Qu.:328.0
## Max. :365.0
##
## PubDate.wkday.fctr PubDate.wkend PubDate.hlday
## 0: 378 Min. :0.0000 Min. :0.0000
## 1:1605 1st Qu.:0.0000 1st Qu.:0.0000
## 2:1559 Median :0.0000 Median :0.0000
## 3:1614 Mean :0.0732 Mean :0.0288
## 4:1539 3rd Qu.:0.0000 3rd Qu.:0.0000
## 5:1470 Max. :1.0000 Max. :1.0000
## 6: 237
## PubDate.hour.fctr PubDate.minute.fctr PubDate.second.fctr
## (-0.023,7.67]:1610 (-0.059,14.8]:3119 (-0.059,14.8]:2134
## (7.67,15.3] :4484 (14.8,29.5] :1671 (14.8,29.5] :2063
## (15.3,23] :2308 (29.5,44.2] :1995 (29.5,44.2] :2112
## (44.2,59.1] :1617 (44.2,59.1] :2093
##
##
##
## PubDate.day.minutes PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.2
## Min. : 0.0 Min. :-0.0274946 Min. :-0.008759
## 1st Qu.: 540.0 1st Qu.:-0.0078858 1st Qu.:-0.007794
## Median : 765.0 Median : 0.0002845 Median :-0.003965
## Mean : 757.2 Mean : 0.0000000 Mean : 0.000000
## 3rd Qu.: 977.8 3rd Qu.: 0.0080100 3rd Qu.: 0.002979
## Max. :1439.0 Max. : 0.0247592 Max. : 0.042684
##
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## Min. :-0.045125 Min. :-0.0183274
## 1st Qu.:-0.007780 1st Qu.:-0.0085001
## Median :-0.000337 Median : 0.0007244
## Mean : 0.000000 Mean : 0.0000000
## 3rd Qu.: 0.007554 3rd Qu.: 0.0081632
## Max. : 0.052153 Max. : 0.0667744
##
## PubDate.day.minutes.poly.5 .order PubDate.last2.log1p
## Min. :-0.0245092 Min. : 1 Min. : 0.6931
## 1st Qu.:-0.0082017 1st Qu.:2101 1st Qu.: 6.5392
## Median :-0.0003821 Median :4202 Median : 7.1835
## Mean : 0.0000000 Mean :4202 Mean : 7.1698
## 3rd Qu.: 0.0077006 3rd Qu.:6302 3rd Qu.: 7.7932
## Max. : 0.0847176 Max. :8402 Max. :10.9120
## NA's :2
## PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p
## Min. : 4.159 Min. : 6.667 Min. : 8.001
## 1st Qu.: 7.484 1st Qu.: 8.259 1st Qu.: 9.030
## Median : 7.923 Median : 8.626 Median : 9.343
## Mean : 8.039 Mean : 8.817 Mean : 9.567
## 3rd Qu.: 8.465 3rd Qu.: 9.191 3rd Qu.:10.126
## Max. :11.243 Max. :11.622 Max. :11.957
## NA's :4 NA's :8 NA's :16
## PubDate.last32.log1p
## Min. : 8.836
## 1st Qu.: 9.784
## Median :10.121
## Mean :10.331
## 3rd Qu.:10.801
## Max. :12.323
## NA's :32
##
## iter imp variable
## 1 1 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 1 2 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 1 3 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 1 4 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 1 5 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 2 1 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 2 2 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 2 3 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 2 4 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 2 5 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 3 1 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 3 2 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 3 3 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 3 4 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 3 5 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 4 1 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 4 2 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 4 3 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 4 4 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 4 5 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 5 1 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 5 2 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 5 3 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 5 4 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## 5 5 PubDate.last2.log1p PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p PubDate.last32.log1p
## WordCount UniqueID .rnorm WordCount.log1p
## Min. : 0.0 Min. : 1 Min. :-3.89398 Min. :0.000
## 1st Qu.: 188.0 1st Qu.:2101 1st Qu.:-0.65896 1st Qu.:5.242
## Median : 377.0 Median :4202 Median : 0.02058 Median :5.935
## Mean : 528.8 Mean :4202 Mean : 0.01549 Mean :5.751
## 3rd Qu.: 735.0 3rd Qu.:6302 3rd Qu.: 0.67764 3rd Qu.:6.601
## Max. :10912.0 Max. :8402 Max. : 3.74468 Max. :9.298
##
## WordCount.root2 WordCount.nexp NDSSName.my.fctr
## Min. : 0.00 Min. :0.00000 ## :1626
## 1st Qu.: 13.71 1st Qu.:0.00000 Bsnss#BsnssDy#Dlbk:1256
## Median : 19.42 Median :0.00000 Cltr#Arts# : 849
## Mean : 20.60 Mean :0.01318 TStyl## : 829
## 3rd Qu.: 27.11 3rd Qu.:0.00000 OpEd#Opnn# : 690
## Max. :104.46 Max. :1.00000 Bsnss#Tchnlgy# : 453
## (Other) :2699
## PubDate.year.fctr PubDate.month.fctr PubDate.date.fctr PubDate.juliandate
## 2014:8402 09:2341 (0.97,7]:1981 Min. :244.0
## 10:2382 (7,13] :1757 1st Qu.:270.0
## 11:1809 (13,19] :1808 Median :297.0
## 12:1870 (19,25] :1650 Mean :300.1
## (25,31] :1206 3rd Qu.:328.0
## Max. :365.0
##
## PubDate.wkday.fctr PubDate.wkend PubDate.hlday
## 0: 378 Min. :0.0000 Min. :0.0000
## 1:1605 1st Qu.:0.0000 1st Qu.:0.0000
## 2:1559 Median :0.0000 Median :0.0000
## 3:1614 Mean :0.0732 Mean :0.0288
## 4:1539 3rd Qu.:0.0000 3rd Qu.:0.0000
## 5:1470 Max. :1.0000 Max. :1.0000
## 6: 237
## PubDate.hour.fctr PubDate.minute.fctr PubDate.second.fctr
## (-0.023,7.67]:1610 (-0.059,14.8]:3119 (-0.059,14.8]:2134
## (7.67,15.3] :4484 (14.8,29.5] :1671 (14.8,29.5] :2063
## (15.3,23] :2308 (29.5,44.2] :1995 (29.5,44.2] :2112
## (44.2,59.1] :1617 (44.2,59.1] :2093
##
##
##
## PubDate.day.minutes PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.2
## Min. : 0.0 Min. :-0.0274946 Min. :-0.008759
## 1st Qu.: 540.0 1st Qu.:-0.0078858 1st Qu.:-0.007794
## Median : 765.0 Median : 0.0002845 Median :-0.003965
## Mean : 757.2 Mean : 0.0000000 Mean : 0.000000
## 3rd Qu.: 977.8 3rd Qu.: 0.0080100 3rd Qu.: 0.002979
## Max. :1439.0 Max. : 0.0247592 Max. : 0.042684
##
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## Min. :-0.045125 Min. :-0.0183274
## 1st Qu.:-0.007780 1st Qu.:-0.0085001
## Median :-0.000337 Median : 0.0007244
## Mean : 0.000000 Mean : 0.0000000
## 3rd Qu.: 0.007554 3rd Qu.: 0.0081632
## Max. : 0.052153 Max. : 0.0667744
##
## PubDate.day.minutes.poly.5 .order PubDate.last2.log1p
## Min. :-0.0245092 Min. : 1 Min. : 0.6931
## 1st Qu.:-0.0082017 1st Qu.:2101 1st Qu.: 6.5396
## Median :-0.0003821 Median :4202 Median : 7.1839
## Mean : 0.0000000 Mean :4202 Mean : 7.1701
## 3rd Qu.: 0.0077006 3rd Qu.:6302 3rd Qu.: 7.7941
## Max. : 0.0847176 Max. :8402 Max. :10.9120
##
## PubDate.last4.log1p PubDate.last8.log1p PubDate.last16.log1p
## Min. : 4.159 Min. : 6.667 Min. : 8.001
## 1st Qu.: 7.484 1st Qu.: 8.259 1st Qu.: 9.030
## Median : 7.923 Median : 8.627 Median : 9.344
## Mean : 8.040 Mean : 8.818 Mean : 9.569
## 3rd Qu.: 8.466 3rd Qu.: 9.193 3rd Qu.:10.130
## Max. :11.243 Max. :11.622 Max. :11.957
##
## PubDate.last32.log1p
## Min. : 8.836
## 1st Qu.: 9.785
## Median :10.125
## Mean :10.334
## 3rd Qu.:10.804
## Max. :12.323
## ## label step_major step_minor label_minor
## 3 extract.features_xtract.DateTime.vars 3 0 0
## 4 extract.features_end 4 0 0
## bgn end elapsed
## 3 24.538 58.264 33.726
## 4 58.265 NA NA## label step_major step_minor label_minor
## 3 extract.features_xtract.DateTime.vars 3 0 0
## 2 extract.features_factorize.str.vars 2 0 0
## 1 extract.features_bgn 1 0 0
## bgn end elapsed duration
## 3 24.538 58.264 33.726 33.726
## 2 24.520 24.537 0.017 0.017
## 1 24.509 24.519 0.010 0.010
## [1] "Total Elapsed Time: 58.264 secs"
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 5 extract.features 3 0 0 24.453 59.576
## 6 manage.missing.data 3 1 1 59.576 NA
## elapsed
## 5 35.123
## 6 NAStep 3.1: manage missing data
## [1] "numeric data missing in : "
## Popular Pplr.fctr
## 1870 1870
## [1] "numeric data w/ 0s in : "
## WordCount Popular WordCount.log1p
## 109 5439 109
## WordCount.root2 WordCount.nexp PubDate.wkday.fctr
## 109 2044 378
## PubDate.wkend PubDate.hlday PubDate.day.minutes
## 7787 8160 5
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate NDSSName.my
## 17 0 0## [1] "numeric data missing in : "
## Popular Pplr.fctr
## 1870 1870
## [1] "numeric data w/ 0s in : "
## WordCount Popular WordCount.log1p
## 109 5439 109
## WordCount.root2 WordCount.nexp PubDate.wkday.fctr
## 109 2044 378
## PubDate.wkend PubDate.hlday PubDate.day.minutes
## 7787 8160 5
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate NDSSName.my
## 17 0 0## label step_major step_minor label_minor bgn end
## 6 manage.missing.data 3 1 1 59.576 60.676
## 7 cluster.data 3 2 2 60.677 NA
## elapsed
## 6 1.101
## 7 NAStep 3.2: cluster data
mycompute_entropy_df <- function(obs_df, entropy_var, by_var=NULL) {
require(lazyeval)
require(dplyr)
require(tidyr)
if (is.null(by_var)) {
by_var <- ".default"
obs_df$.default <- as.factor(".default")
}
if (!any(grepl(".clusterid", names(obs_df), fixed=TRUE)))
obs_df$.clusterid <- 1
cluster_df <- obs_df %>%
count_(c(by_var, ".clusterid", entropy_var)) %>%
dplyr::filter(n > 0) %>%
dplyr::filter_(interp(~(!is.na(var)), var=as.name(entropy_var))) %>%
unite_(paste0(by_var, ".clusterid"),
c(interp(by_var), ".clusterid")) %>%
spread_(interp(entropy_var), "n", fill=0)
# head(cluster_df)
# sum(cluster_df$n)
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method = "ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
#print(cluster_df)
return(cluster_df)
}
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
require(ggdendro)
mywgtdcosine_dist <- function(x, y=NULL, weights=NULL) {
if (!inherits(x, "matrix"))
x <- as.matrix(x)
if (is.null(weights))
weights <- rep(1, ncol(x))
wgtsx <- matrix(rep(weights / sum(weights), nrow(x)), nrow = nrow(x),
byrow = TRUE)
wgtdx <- x * wgtsx
wgtdxsqsum <- as.matrix(rowSums((x ^ 2) * wgtsx), byrow=FALSE)
denom <- sqrt(wgtdxsqsum %*% t(wgtdxsqsum))
ret_mtrx <- 1 - ((sum(weights) ^ 1) * (wgtdx %*% t(wgtdx)) / denom)
ret_mtrx[is.nan(ret_mtrx)] <- 1
diag(ret_mtrx) <- 0
return(ret_mtrx)
}
#pr_DB$delete_entry("mywgtdcosine");
# Need to do this only once across runs ?
if (!pr_DB$entry_exists("mywgtdcosine")) {
pr_DB$set_entry(FUN = mywgtdcosine_dist, names = c("mywgtdcosine"))
pr_DB$modify_entry(names="mywgtdcosine", type="metric", loop=FALSE)
}
#pr_DB$get_entry("mywgtdcosine")
# glb_hash <- hash(key=unique(glbObsAll$myCategory),
# values=1:length(unique(glbObsAll$myCategory)))
# glb_hash_lst <- hash(key=unique(glbObsAll$myCategory),
# values=1:length(unique(glbObsAll$myCategory)))
#stop(here"); glb2Sav(); glbObsAll <- savObsAll
cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
"]\\.[PT]\\."),
names(glbObsAll), value = TRUE)
# Assign correlations with rsp_var as weights for cosine distance
print("Clustering features: ")
cluster_vars_df <- data.frame(abs.cor.y = abs(cor(
glbObsAll[glbObsAll$.src == "Train", cluster_vars],
as.numeric(glbObsAll[glbObsAll$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs")))
print(tail(cluster_vars_df <- orderBy(~ abs.cor.y,
subset(cluster_vars_df, !is.na(abs.cor.y))), 5))
print(sprintf(" .rnorm cor: %0.4f",
cor(glbObsAll[glbObsAll$.src == "Train", ".rnorm"],
as.numeric(glbObsAll[glbObsAll$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs")))
print(sprintf("glbObsAll Entropy: %0.4f",
allobs_ent <- entropy(table(glbObsAll[, glb_cluster_entropy_var]),
method="ML")))
print(category_df <- mycompute_entropy_df(obs_df=glbObsAll,
entropy_var=glb_cluster_entropy_var,
by_var=glbFeatsCategory))
print(sprintf("glbObsAll$%s Entropy: %0.4f (%0.4f pct)",
glbFeatsCategory,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
glbObsAll$.clusterid <- 1
#print(max(table(glbObsAll$myCategory.fctr) / 20))
#stop(here"); glb2Sav()
grp_ids <- sort(unique(glbObsAll[, glbFeatsCategory]))
glb_cluster_size_df_lst <- list()
png(paste0(glb_out_pfx, "FeatsTxtClusters.png"),
width = 480 * 2, height = 480 * length(grp_ids))
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow = length(grp_ids), ncol = 2)))
pltIx <- 1
for (grp in grp_ids) {
# if (grep(grp, levels(grp_ids)) <= 6) next
# if (grep(grp, levels(grp_ids)) > 9) next
# if (grep(grp, levels(grp_ids)) != 10) next
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glbObsAll[glbObsAll[, glbFeatsCategory] == grp, ]
if (!inherits(ctgry_allobs_df[, glb_cluster_entropy_var], "factor"))
ctgry_allobs_df[, glb_cluster_entropy_var] <-
as.factor(ctgry_allobs_df[, glb_cluster_entropy_var])
#dstns_dist <- proxy::dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_dist <- proxy::dist(ctgry_allobs_df[, row.names(cluster_vars_df)],
method = "mywgtdcosine",
weights = cluster_vars_df$abs.cor.y)
# Custom distance functions return a crossdist object
#dstns_mtrx <- as.matrix(dstns_dist)
dstns_mtrx <- matrix(as.vector(dstns_dist), nrow=attr(dstns_dist, "dim")[1],
dimnames=attr(dstns_dist, "dimnames"))
dstns_dist <- as.dist(dstns_mtrx)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
# print(dim(dstns_mtrx))
# print(sprintf("which.max: %d", which.max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
# print(sprintf("row_ix: %d", row_ix)); print(sprintf("col_ix: %d", col_ix));
# print(dim(ctgry_allobs_df))
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glbFeatsCategory, glbFeatsText, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
# Float representations issue -2.22e-16 vs. 0.0000
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glbFeatsCategory, glbFeatsText,
cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
# Workaround to avoid "Error in cutree(dendro, h = heightcutoff) : the 'height' component of 'tree' is not sorted (increasingly)"
if (with(clusters,all.equal(height,sort(height))))
clusters$height <- round(clusters$height,6)
clusters$labels <- ctgry_allobs_df[, glb_id_var]
clustersDD <- dendro_data(clusters)
clustersDD$labels[, glb_rsp_var] <- sapply(clustersDD$labels$label, function(id)
ctgry_allobs_df[id == ctgry_allobs_df[, glb_id_var], glb_rsp_var])
print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
geom_point(data = clustersDD$labels,
aes_string(x = "x", color = glb_rsp_var), y = min(clustersDD$segments$y)) +
coord_flip(ylim = c(min(clustersDD$segments$y),
max(clustersDD$segments$y))) +
ggtitle(grp),
vp = viewport(layout.pos.row = pltIx, layout.pos.col = 1))
# clusters$labels <- ctgry_allobs_df[, glb_id_var]
# clustersDD <- dendro_data(clusters)
# clustersDD$labels$color <- sapply(clustersDD$labels$label, function(id)
# ctgry_allobs_df[id == ctgry_allobs_df[, glb_id_var], glb_rsp_var])
# print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
# geom_point(data = clustersDD$labels,
# aes_string(x = "x", color = "color"), y = min(clustersDD$segments$y)) +
# coord_flip(ylim = c(min(clustersDD$segments$y),
# max(clustersDD$segments$y))))
# print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
# geom_point(data = clustersDD$labels,
# aes_string(x = "x", y = "y", color = "color")))
# myplclust(clusters, lab=ctgry_allobs_df[, glb_id_var],
# lab.col=unclass(ctgry_allobs_df[, glb_cluster_entropy_var]))
opt_minclustersize_df <- data.frame(minclustersize = nrow(ctgry_allobs_df),
entropy = entropy(table(ctgry_allobs_df[, glb_cluster_entropy_var]),
method = "ML"))
for (minclustersize in
as.integer(seq(nrow(ctgry_allobs_df) / 2, nrow(ctgry_allobs_df) / 10,
length = 5))) {
clusterGroups <- cutreeDynamic(clusters, minClusterSize = minclustersize,
method = "tree", deepSplit = 0)
# Unassigned groups are labeled 0; the largest group has label 1
clusterGroups[clusterGroups == 0] <- 1
ctgry_allobs_df$.clusterid <- clusterGroups
ctgry_clstrs_df <- mycompute_entropy_df(ctgry_allobs_df,
glb_cluster_entropy_var)
opt_minclustersize_df <- rbind(opt_minclustersize_df,
data.frame(minclustersize = minclustersize,
entropy = weighted.mean(ctgry_clstrs_df$.entropy, ctgry_clstrs_df$.knt)))
}
opt_minclustersize <-
opt_minclustersize_df$minclustersize[which.min(opt_minclustersize_df$entropy)]
opt_minclustersize_df$.color <-
ifelse(opt_minclustersize_df$minclustersize == opt_minclustersize,
"red", "blue")
print(ggplot(data = opt_minclustersize_df,
mapping = aes(x = minclustersize, y = entropy)) +
geom_point(aes(color = .color)) + scale_color_identity() +
guides(color = "none") + geom_line(),
vp = viewport(layout.pos.row = pltIx, layout.pos.col = 2))
glb_cluster_size_df_lst[[grp]] <- opt_minclustersize_df
# select minclustersize that minimizes entropy
clusterGroups <- cutreeDynamic(clusters, minClusterSize = opt_minclustersize,
method = "tree",
deepSplit = 0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var],
useNA = "ifany")
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA = "ifany")
glbObsAll[glbObsAll[, glbFeatsCategory] == grp,]$.clusterid <-
clusterGroups
pltIx <- pltIx + 1
}
dev.off()
#all.equal(savObsAll_clusterid, glbObsAll$.clusterid)
print(cluster_df <- mycompute_entropy_df(obs_df=glbObsAll,
entropy_var=glb_cluster_entropy_var,
by_var=glbFeatsCategory))
print(sprintf("glbObsAll$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glbFeatsCategory,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
glbObsAll$.clusterid.fctr <- as.factor(glbObsAll$.clusterid)
# .clusterid.fctr is created automatically (probably ?) later
glbFeatsExclude <- c(glbFeatsExclude, ".clusterid")
if (!is.null(glbFeatsCategory))
# glbFeatsInteractionOnly[ifelse(grepl("\\.fctr", glbFeatsCategory),
# glbFeatsCategory,
# paste0(glbFeatsCategory, ".fctr"))] <-
# c(".clusterid.fctr")
glbFeatsInteractionOnly[[".clusterid.fctr"]] <-
ifelse(grepl("\\.fctr", glbFeatsCategory), glbFeatsCategory,
paste0(glbFeatsCategory, ".fctr"))
if (glbFeatsTextClusterVarsExclude)
glbFeatsExclude <- c(glbFeatsExclude, cluster_vars)
}
# Last call for data modifications
#stop(here") # savObsAll <- glbObsAll
# glbObsAll[(glbObsAll$PropR == 0.75) & (glbObsAll$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glbObsTrn <- subset(glbObsAll, .src == "Train")
glbObsNew <- subset(glbObsAll, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 7 cluster.data 3 2 2 60.677 60.745
## 8 partition.data.training 4 0 0 60.746 NA
## elapsed
## 7 0.068
## 8 NAStep 4.0: partition data training
## [1] "Prediction Hints by Catgeory:"
## NDSSName.my.fctr Popular.0 Popular.1 .n.tst .strata.0 .strata.1
## 5 #U.S.#Edctn 325 NA 89 82 17
## 10 Cltr## 1 NA 70 1 13
## 12 Frgn#Wrld# 172 NA 47 44 9
## 21 myOthr 38 NA 5 5 1## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## cluster## Popular.0 Popular.1 Popular.NA
## NA NA 1870
## Fit 3941 863 NA
## OOB 1498 230 NA
## Popular.0 Popular.1 Popular.NA
## NA NA 1
## Fit 0.8203580 0.1796420 NA
## OOB 0.8668981 0.1331019 NA
## NDSSName.my.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit
## 1 ## 913 371 342 0.190049958
## 6 Bsnss#BsnssDy#Dlbk 629 323 304 0.130932556
## 11 Cltr#Arts# 490 185 174 0.101998335
## 15 OpEd#Opnn# 437 89 164 0.090965862
## 9 Bsnss#Tchnlgy# 213 126 114 0.044338052
## 19 TStyl## 623 101 105 0.129683597
## 5 #U.S.#Edctn 243 82 89 0.050582848
## 10 Cltr## NA 1 70 NA
## 14 Mtr#N.Y./Rgn# 128 70 67 0.026644463
## 18 Styls#U.S.# 127 50 61 0.026436303
## 16 Scnc#Hlth# 148 48 57 0.030807660
## 13 Frgn#Wrld#AsPcfc 150 53 56 0.031223980
## 2 #Mltmd# 92 49 52 0.019150708
## 12 Frgn#Wrld# 128 44 47 0.026644463
## 8 Bsnss#Crsswrds/Gms# 105 18 42 0.021856786
## 7 Bsnss#BsnssDy#SmllBsnss 100 40 41 0.020815987
## 20 Trvl#Trvl# 83 34 35 0.017277269
## 3 #Opnn#RmFrDbt 42 20 20 0.008742714
## 17 Styls##Fshn 104 15 15 0.021648626
## 4 #Opnn#ThPblcEdtr 16 4 10 0.003330558
## 21 myOthr 33 5 5 0.006869276
## .freqRatio.OOB .freqRatio.Tst
## 1 0.2146990741 0.182887701
## 6 0.1869212963 0.162566845
## 11 0.1070601852 0.093048128
## 15 0.0515046296 0.087700535
## 9 0.0729166667 0.060962567
## 19 0.0584490741 0.056149733
## 5 0.0474537037 0.047593583
## 10 0.0005787037 0.037433155
## 14 0.0405092593 0.035828877
## 18 0.0289351852 0.032620321
## 16 0.0277777778 0.030481283
## 13 0.0306712963 0.029946524
## 2 0.0283564815 0.027807487
## 12 0.0254629630 0.025133690
## 8 0.0104166667 0.022459893
## 7 0.0231481481 0.021925134
## 20 0.0196759259 0.018716578
## 3 0.0115740741 0.010695187
## 17 0.0086805556 0.008021390
## 4 0.0023148148 0.005347594
## 21 0.0028935185 0.002673797## [1] "glbObsAll: "## [1] 8402 42## [1] "glbObsTrn: "## [1] 6532 42## [1] "glbObsFit: "## [1] 4804 41## [1] "glbObsOOB: "## [1] 1728 41## [1] "glbObsNew: "## [1] 1870 41## Warning in rm(split): object 'split' not found## label step_major step_minor label_minor bgn end
## 8 partition.data.training 4 0 0 60.746 62.06
## 9 select.features 5 0 0 62.061 NA
## elapsed
## 8 1.314
## 9 NAStep 5.0: select features
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## Popular Popular 1.000000000
## WordCount.root2 WordCount.root2 0.292120679
## WordCount WordCount 0.257526549
## WordCount.log1p WordCount.log1p 0.254319628
## NDSSName.my.fctr NDSSName.my.fctr 0.165445970
## PubDate.day.minutes PubDate.day.minutes 0.156753478
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.1 0.156753478
## PubDate.hour.fctr PubDate.hour.fctr 0.135436805
## PubDate.wkend PubDate.wkend 0.104707290
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 0.073941394
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.2 0.070977720
## PubDate.last4.log1p PubDate.last4.log1p 0.069776398
## PubDate.last2.log1p PubDate.last2.log1p 0.065679536
## PubDate.last8.log1p PubDate.last8.log1p 0.056574578
## PubDate.day.minutes.poly.5 PubDate.day.minutes.poly.5 -0.055929231
## WordCount.nexp WordCount.nexp -0.053208396
## PubDate.wkday.fctr PubDate.wkday.fctr -0.039801288
## PubDate.last16.log1p PubDate.last16.log1p 0.038456811
## PubDate.minute.fctr PubDate.minute.fctr -0.034073846
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.3 0.027983551
## PubDate.last32.log1p PubDate.last32.log1p 0.022542505
## PubDate.month.fctr PubDate.month.fctr 0.019148739
## PubDate.POSIX PubDate.POSIX 0.015683258
## PubDate.hlday PubDate.hlday 0.014690122
## PubDate.juliandate PubDate.juliandate 0.014361075
## PubDate.zoo PubDate.zoo 0.013260902
## PubDate.second.fctr PubDate.second.fctr -0.011879458
## UniqueID UniqueID 0.011824920
## PubDate.date.fctr PubDate.date.fctr -0.011647558
## .rnorm .rnorm 0.008212201
## PubDate.year.fctr PubDate.year.fctr NA
## exclude.as.feat cor.y.abs
## Popular 1 1.000000000
## WordCount.root2 0 0.292120679
## WordCount 1 0.257526549
## WordCount.log1p 0 0.254319628
## NDSSName.my.fctr 0 0.165445970
## PubDate.day.minutes 1 0.156753478
## PubDate.day.minutes.poly.1 0 0.156753478
## PubDate.hour.fctr 0 0.135436805
## PubDate.wkend 0 0.104707290
## PubDate.day.minutes.poly.4 0 0.073941394
## PubDate.day.minutes.poly.2 0 0.070977720
## PubDate.last4.log1p 0 0.069776398
## PubDate.last2.log1p 0 0.065679536
## PubDate.last8.log1p 0 0.056574578
## PubDate.day.minutes.poly.5 0 0.055929231
## WordCount.nexp 0 0.053208396
## PubDate.wkday.fctr 0 0.039801288
## PubDate.last16.log1p 0 0.038456811
## PubDate.minute.fctr 0 0.034073846
## PubDate.day.minutes.poly.3 0 0.027983551
## PubDate.last32.log1p 0 0.022542505
## PubDate.month.fctr 0 0.019148739
## PubDate.POSIX 1 0.015683258
## PubDate.hlday 0 0.014690122
## PubDate.juliandate 0 0.014361075
## PubDate.zoo 1 0.013260902
## PubDate.second.fctr 0 0.011879458
## UniqueID 1 0.011824920
## PubDate.date.fctr 0 0.011647558
## .rnorm 0 0.008212201
## PubDate.year.fctr 0 NA## [1] "cor(PubDate.juliandate, PubDate.month.fctr)=0.9393"
## [1] "cor(Pplr.fctr, PubDate.juliandate)=0.0144"
## [1] "cor(Pplr.fctr, PubDate.month.fctr)=0.0191"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified PubDate.juliandate as highly correlated with
## PubDate.month.fctr## [1] "cor(PubDate.day.minutes.poly.1, PubDate.hour.fctr)=0.9026"
## [1] "cor(Pplr.fctr, PubDate.day.minutes.poly.1)=0.1568"
## [1] "cor(Pplr.fctr, PubDate.hour.fctr)=0.1354"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified PubDate.hour.fctr as highly correlated with
## PubDate.day.minutes.poly.1## [1] "cor(PubDate.last16.log1p, PubDate.last8.log1p)=0.8942"
## [1] "cor(Pplr.fctr, PubDate.last16.log1p)=0.0385"
## [1] "cor(Pplr.fctr, PubDate.last8.log1p)=0.0566"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified PubDate.last16.log1p as highly correlated with
## PubDate.last8.log1p## [1] "cor(WordCount.log1p, WordCount.root2)=0.8906"
## [1] "cor(Pplr.fctr, WordCount.log1p)=0.2543"
## [1] "cor(Pplr.fctr, WordCount.root2)=0.2921"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified WordCount.log1p as highly correlated with
## WordCount.root2## [1] "cor(PubDate.last4.log1p, PubDate.last8.log1p)=0.8579"
## [1] "cor(Pplr.fctr, PubDate.last4.log1p)=0.0698"
## [1] "cor(Pplr.fctr, PubDate.last8.log1p)=0.0566"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified PubDate.last8.log1p as highly correlated with
## PubDate.last4.log1p## [1] "cor(PubDate.last2.log1p, PubDate.last4.log1p)=0.7709"
## [1] "cor(Pplr.fctr, PubDate.last2.log1p)=0.0657"
## [1] "cor(Pplr.fctr, PubDate.last4.log1p)=0.0698"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified PubDate.last2.log1p as highly correlated with
## PubDate.last4.log1p## id cor.y
## Popular Popular 1.000000000
## WordCount.root2 WordCount.root2 0.292120679
## WordCount WordCount 0.257526549
## WordCount.log1p WordCount.log1p 0.254319628
## NDSSName.my.fctr NDSSName.my.fctr 0.165445970
## PubDate.day.minutes PubDate.day.minutes 0.156753478
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.1 0.156753478
## PubDate.hour.fctr PubDate.hour.fctr 0.135436805
## PubDate.wkend PubDate.wkend 0.104707290
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 0.073941394
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.2 0.070977720
## PubDate.last4.log1p PubDate.last4.log1p 0.069776398
## PubDate.last2.log1p PubDate.last2.log1p 0.065679536
## PubDate.last8.log1p PubDate.last8.log1p 0.056574578
## PubDate.last16.log1p PubDate.last16.log1p 0.038456811
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.3 0.027983551
## PubDate.last32.log1p PubDate.last32.log1p 0.022542505
## PubDate.month.fctr PubDate.month.fctr 0.019148739
## PubDate.POSIX PubDate.POSIX 0.015683258
## PubDate.hlday PubDate.hlday 0.014690122
## PubDate.juliandate PubDate.juliandate 0.014361075
## PubDate.zoo PubDate.zoo 0.013260902
## UniqueID UniqueID 0.011824920
## .rnorm .rnorm 0.008212201
## PubDate.date.fctr PubDate.date.fctr -0.011647558
## PubDate.second.fctr PubDate.second.fctr -0.011879458
## PubDate.minute.fctr PubDate.minute.fctr -0.034073846
## PubDate.wkday.fctr PubDate.wkday.fctr -0.039801288
## WordCount.nexp WordCount.nexp -0.053208396
## PubDate.day.minutes.poly.5 PubDate.day.minutes.poly.5 -0.055929231
## PubDate.year.fctr PubDate.year.fctr NA
## exclude.as.feat cor.y.abs
## Popular 1 1.000000000
## WordCount.root2 0 0.292120679
## WordCount 1 0.257526549
## WordCount.log1p 0 0.254319628
## NDSSName.my.fctr 0 0.165445970
## PubDate.day.minutes 1 0.156753478
## PubDate.day.minutes.poly.1 0 0.156753478
## PubDate.hour.fctr 0 0.135436805
## PubDate.wkend 0 0.104707290
## PubDate.day.minutes.poly.4 0 0.073941394
## PubDate.day.minutes.poly.2 0 0.070977720
## PubDate.last4.log1p 0 0.069776398
## PubDate.last2.log1p 0 0.065679536
## PubDate.last8.log1p 0 0.056574578
## PubDate.last16.log1p 0 0.038456811
## PubDate.day.minutes.poly.3 0 0.027983551
## PubDate.last32.log1p 0 0.022542505
## PubDate.month.fctr 0 0.019148739
## PubDate.POSIX 1 0.015683258
## PubDate.hlday 0 0.014690122
## PubDate.juliandate 0 0.014361075
## PubDate.zoo 1 0.013260902
## UniqueID 1 0.011824920
## .rnorm 0 0.008212201
## PubDate.date.fctr 0 0.011647558
## PubDate.second.fctr 0 0.011879458
## PubDate.minute.fctr 0 0.034073846
## PubDate.wkday.fctr 0 0.039801288
## WordCount.nexp 0 0.053208396
## PubDate.day.minutes.poly.5 0 0.055929231
## PubDate.year.fctr 0 NA
## cor.high.X freqRatio
## Popular <NA> 4.976212
## WordCount.root2 <NA> 2.315789
## WordCount <NA> 2.315789
## WordCount.log1p WordCount.root2 2.315789
## NDSSName.my.fctr <NA> 1.348739
## PubDate.day.minutes <NA> 1.225490
## PubDate.day.minutes.poly.1 <NA> 1.225490
## PubDate.hour.fctr PubDate.day.minutes.poly.1 1.835040
## PubDate.wkend <NA> 12.011952
## PubDate.day.minutes.poly.4 <NA> 1.225490
## PubDate.day.minutes.poly.2 <NA> 1.225490
## PubDate.last4.log1p <NA> 1.125000
## PubDate.last2.log1p PubDate.last4.log1p 1.375000
## PubDate.last8.log1p PubDate.last4.log1p 1.166667
## PubDate.last16.log1p PubDate.last8.log1p 1.000000
## PubDate.day.minutes.poly.3 <NA> 1.225490
## PubDate.last32.log1p <NA> 1.000000
## PubDate.month.fctr <NA> 1.017514
## PubDate.POSIX <NA> 1.000000
## PubDate.hlday <NA> 28.160714
## PubDate.juliandate PubDate.month.fctr 1.032520
## PubDate.zoo <NA> 1.000000
## UniqueID <NA> 1.000000
## .rnorm <NA> 1.000000
## PubDate.date.fctr <NA> 1.021394
## PubDate.second.fctr <NA> 1.018204
## PubDate.minute.fctr <NA> 1.483365
## PubDate.wkday.fctr <NA> 1.003268
## WordCount.nexp <NA> 17.761364
## PubDate.day.minutes.poly.5 <NA> 1.225490
## PubDate.year.fctr <NA> 0.000000
## percentUnique zeroVar nzv is.cor.y.abs.low
## Popular 0.03061849 FALSE FALSE FALSE
## WordCount.root2 24.15799143 FALSE FALSE FALSE
## WordCount 24.15799143 FALSE FALSE FALSE
## WordCount.log1p 24.15799143 FALSE FALSE FALSE
## NDSSName.my.fctr 0.32149418 FALSE FALSE FALSE
## PubDate.day.minutes 18.08022045 FALSE FALSE FALSE
## PubDate.day.minutes.poly.1 18.08022045 FALSE FALSE FALSE
## PubDate.hour.fctr 0.04592774 FALSE FALSE FALSE
## PubDate.wkend 0.03061849 FALSE FALSE FALSE
## PubDate.day.minutes.poly.4 18.08022045 FALSE FALSE FALSE
## PubDate.day.minutes.poly.2 18.08022045 FALSE FALSE FALSE
## PubDate.last4.log1p 64.98775260 FALSE FALSE FALSE
## PubDate.last2.log1p 51.16350276 FALSE FALSE FALSE
## PubDate.last8.log1p 75.15309247 FALSE FALSE FALSE
## PubDate.last16.log1p 84.50704225 FALSE FALSE FALSE
## PubDate.day.minutes.poly.3 18.08022045 FALSE FALSE FALSE
## PubDate.last32.log1p 91.13594611 FALSE FALSE FALSE
## PubDate.month.fctr 0.04592774 FALSE FALSE FALSE
## PubDate.POSIX 99.86221678 FALSE FALSE FALSE
## PubDate.hlday 0.03061849 FALSE TRUE FALSE
## PubDate.juliandate 1.39314146 FALSE FALSE FALSE
## PubDate.zoo 99.86221678 FALSE FALSE FALSE
## UniqueID 100.00000000 FALSE FALSE FALSE
## .rnorm 100.00000000 FALSE FALSE FALSE
## PubDate.date.fctr 0.07654623 FALSE FALSE FALSE
## PubDate.second.fctr 0.06123699 FALSE FALSE FALSE
## PubDate.minute.fctr 0.06123699 FALSE FALSE FALSE
## PubDate.wkday.fctr 0.10716473 FALSE FALSE FALSE
## WordCount.nexp 11.32884262 FALSE FALSE FALSE
## PubDate.day.minutes.poly.5 18.08022045 FALSE FALSE FALSE
## PubDate.year.fctr 0.01530925 TRUE TRUE NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).
## id cor.y exclude.as.feat cor.y.abs
## PubDate.hlday PubDate.hlday 0.01469012 0 0.01469012
## PubDate.year.fctr PubDate.year.fctr NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## PubDate.hlday <NA> 28.16071 0.03061849 FALSE TRUE
## PubDate.year.fctr <NA> 0.00000 0.01530925 TRUE TRUE
## is.cor.y.abs.low
## PubDate.hlday FALSE
## PubDate.year.fctr NA
## +(rfe) fit Fold1.Rep1 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 55
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## +(rfe) imp Fold3.Rep3
## -(rfe) imp Fold3.Rep3
## +(rfe) fit Fold3.Rep3 size: 32
## -(rfe) fit Fold3.Rep3 size: 32
## +(rfe) fit Fold3.Rep3 size: 16
## -(rfe) fit Fold3.Rep3 size: 16
## +(rfe) fit Fold3.Rep3 size: 8
## -(rfe) fit Fold3.Rep3 size: 8
## +(rfe) fit Fold3.Rep3 size: 4
## -(rfe) fit Fold3.Rep3 size: 4
## +(rfe) fit Fold3.Rep3 size: 2
## -(rfe) fit Fold3.Rep3 size: 2## Warning in lda.default(x, grouping, ...): variables are collinear##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 2 0.8096 0.03708 0.005112 0.01788
## 4 0.8864 0.52518 0.005536 0.02625
## 8 0.8944 0.57831 0.013981 0.06890
## 16 0.9305 0.75939 0.004671 0.01720
## 32 0.9303 0.75899 0.004681 0.01717
## 55 0.9326 0.76882 0.004814 0.01705 *
##
## The top 5 variables (out of 55):
## WordCount.log1p, WordCount.root2, WordCount.nexp, NDSSName.my.fctrOpEd#Opnn#, PubDate.day.minutes.poly.1
##
## [1] "WordCount.log1p"
## [2] "WordCount.root2"
## [3] "WordCount.nexp"
## [4] "NDSSName.my.fctrOpEd#Opnn#"
## [5] "PubDate.day.minutes.poly.1"
## [6] "PubDate.day.minutes.poly.4"
## [7] "PubDate.hour.fctr(15.3,23]"
## [8] "NDSSName.my.fctrScnc#Hlth#"
## [9] "PubDate.last4.log1p"
## [10] "PubDate.last2.log1p"
## [11] "NDSSName.my.fctrBsnss#Crsswrds/Gms#"
## [12] "NDSSName.my.fctrStyls#U.S.#"
## [13] "PubDate.last8.log1p"
## [14] "PubDate.day.minutes.poly.5"
## [15] "PubDate.wkend"
## [16] "PubDate.last16.log1p"
## [17] "PubDate.juliandate"
## [18] "PubDate.month.fctr11"
## [19] "PubDate.day.minutes.poly.3"
## [20] "PubDate.wkday.fctr6"
## [21] "PubDate.date.fctr(7,13]"
## [22] "PubDate.second.fctr(14.8,29.5]"
## [23] "PubDate.month.fctr10"
## [24] "PubDate.wkday.fctr1"
## [25] ".rnorm"
## [26] "PubDate.minute.fctr(44.2,59.1]"
## [27] "PubDate.day.minutes.poly.2"
## [28] "PubDate.hour.fctr(7.67,15.3]"
## [29] "PubDate.minute.fctr(14.8,29.5]"
## [30] "PubDate.date.fctr(25,31]"
## [31] "PubDate.last32.log1p"
## [32] "PubDate.second.fctr(44.2,59.1]"
## [33] "PubDate.wkday.fctr3"
## [34] "NDSSName.my.fctrmyOthr"
## [35] "PubDate.date.fctr(19,25]"
## [36] "NDSSName.my.fctr#Opnn#RmFrDbt"
## [37] "NDSSName.my.fctrBsnss#Tchnlgy#"
## [38] "PubDate.wkday.fctr4"
## [39] "PubDate.second.fctr(29.5,44.2]"
## [40] "PubDate.date.fctr(13,19]"
## [41] "NDSSName.my.fctrMtr#N.Y./Rgn#"
## [42] "NDSSName.my.fctrTrvl#Trvl#"
## [43] "NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss"
## [44] "NDSSName.my.fctr#Mltmd#"
## [45] "PubDate.wkday.fctr2"
## [46] "NDSSName.my.fctrStyls##Fshn"
## [47] "NDSSName.my.fctrFrgn#Wrld#"
## [48] "PubDate.minute.fctr(29.5,44.2]"
## [49] "NDSSName.my.fctrFrgn#Wrld#AsPcfc"
## [50] "PubDate.wkday.fctr5"
## [51] "NDSSName.my.fctr#U.S.#Edctn"
## [52] "NDSSName.my.fctrCltr#Arts#"
## [53] "NDSSName.my.fctrBsnss#BsnssDy#Dlbk"
## [54] "NDSSName.my.fctr##"
## [55] "NDSSName.my.fctrTStyl##"
## [1] "numeric data missing in : "
## Popular Pplr.fctr
## 1870 1870
## [1] "numeric data w/ 0s in : "
## WordCount Popular WordCount.log1p
## 109 5439 109
## WordCount.root2 WordCount.nexp PubDate.wkday.fctr
## 109 2044 378
## PubDate.wkend PubDate.hlday PubDate.day.minutes
## 7787 8160 5
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate NDSSName.my .lcn
## 17 0 0 1870## [1] "glb_feats_df:"## [1] 31 12## id exclude.as.feat rsp_var
## Pplr.fctr Pplr.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## Popular Popular 1.00000000 TRUE 1.00000000 <NA>
## UniqueID UniqueID 0.01182492 TRUE 0.01182492 <NA>
## Pplr.fctr Pplr.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Popular 4.976212 0.03061849 FALSE FALSE FALSE
## UniqueID 1.000000 100.00000000 FALSE FALSE FALSE
## Pplr.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var rsp_var
## Popular NA NA TRUE NA NA
## UniqueID NA NA FALSE TRUE NA
## Pplr.fctr NA NA NA NA TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end elapsed
## 9 select.features 5 0 0 62.061 83.208 21.147
## 10 fit.models 6 0 0 83.208 NA NAStep 6.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 84.325 NA NA# load(paste0(glb_out_pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glb_out_pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
c("id.prefix", "method", "type",
# trainControl params
"preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# train params
"metric", "metric.maximize", "tune.df")## [1] "id.prefix" "method" "type"
## [4] "preProc.method" "cv.n.folds" "cv.n.repeats"
## [7] "summary.fn" "metric" "metric.maximize"
## [10] "tune.df"# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 84.325 84.355
## 2 fit.models_0_MFO 1 1 myMFO_classfr 84.356 NA
## elapsed
## 1 0.031
## 2 NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "fitting model: MFO###myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.820358 0.179642
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.820358 0.179642
## 2 0.820358 0.179642
## 3 0.820358 0.179642
## 4 0.820358 0.179642
## 5 0.820358 0.179642
## 6 0.820358 0.179642
## Prediction
## Reference N Y
## N 0 3941
## Y 0 863
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1796420 0.0000000 0.1688795 0.1907952 0.8203580
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.820358 0.179642
## 2 0.820358 0.179642
## 3 0.820358 0.179642
## 4 0.820358 0.179642
## 5 0.820358 0.179642
## 6 0.820358 0.179642
## Prediction
## Reference N Y
## N 0 1498
## Y 0 230
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1331019 0.0000000 0.1174298 0.1500310 0.8668981
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.296
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.003 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.1 0.3045703 0.179642
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.1688795 0.1907952 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.2349336 0.1331019
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.1174298 0.150031 0if (glb_is_classification) {
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 2 fit.models_0_MFO 1 1 myMFO_classfr 84.356 87.427
## 3 fit.models_0_Random 1 2 myrandom_classfr 87.427 NA
## elapsed
## 2 3.071
## 3 NA
## [1] "fitting model: Random###myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "in Random.Classifier$prob"
## Prediction
## Reference N Y
## N 0 3941
## Y 0 863
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1796420 0.0000000 0.1688795 0.1907952 0.8203580
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "in Random.Classifier$prob"
## Prediction
## Reference N Y
## N 0 1498
## Y 0 230
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1331019 0.0000000 0.1174298 0.1500310 0.8668981
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.298 0.002 0.4990604
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8312611 0.1668598 0.4972757 0.1
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.3045703 0.179642 0.1688795
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.1907952 0 0.5125675 0.8077437
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.2173913 0.4857956 0.1 0.2349336
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.1331019 0.1174298 0.150031
## max.Kappa.OOB
## 1 0# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 87.427 91.766 4.339
## 4 91.766 NA NAret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-2## Fitting alpha = 0.1, lambda = 0.00434 on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 2100 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 21 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -4.57159198
## NDSSName.my.fctr#Mltmd#
## -1.22219085
## NDSSName.my.fctr#Opnn#RmFrDbt
## -3.46072453
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 4.06871185
## NDSSName.my.fctr#U.S.#Edctn
## -1.89443632
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.22472818
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.95537118
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 4.55408513
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.77368538
## NDSSName.my.fctrCltr#Arts#
## -0.09465691
## NDSSName.my.fctrFrgn#Wrld#
## -1.45528874
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -1.60117505
## NDSSName.my.fctrMtr#N.Y./Rgn#
## 0.01563989
## NDSSName.my.fctrOpEd#Opnn#
## 4.51696382
## NDSSName.my.fctrScnc#Hlth#
## 3.51595317
## NDSSName.my.fctrStyls##Fshn
## -1.85948925
## NDSSName.my.fctrStyls#U.S.#
## 3.27995325
## NDSSName.my.fctrTStyl##
## -1.54110404
## NDSSName.my.fctrTrvl#Trvl#
## -1.41940605
## NDSSName.my.fctrmyOthr
## -1.90156922
## WordCount.root2
## 0.08434378
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -4.60394059
## NDSSName.my.fctr#Mltmd#
## -1.25163328
## NDSSName.my.fctr#Opnn#RmFrDbt
## -3.55521332
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 4.09217313
## NDSSName.my.fctr#U.S.#Edctn
## -1.96172971
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.22495986
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.96836050
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 4.58120497
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.78504703
## NDSSName.my.fctrCltr#Arts#
## -0.09069661
## NDSSName.my.fctrFrgn#Wrld#
## -1.51061232
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -1.63313235
## NDSSName.my.fctrMtr#N.Y./Rgn#
## 0.02466697
## NDSSName.my.fctrOpEd#Opnn#
## 4.54361134
## NDSSName.my.fctrScnc#Hlth#
## 3.53210055
## NDSSName.my.fctrStyls##Fshn
## -1.92188290
## NDSSName.my.fctrStyls#U.S.#
## 3.29488750
## NDSSName.my.fctrTStyl##
## -1.57788931
## NDSSName.my.fctrTrvl#Trvl#
## -1.47368131
## NDSSName.my.fctrmyOthr
## -1.97357582
## WordCount.root2
## 0.08537319
## Prediction
## Reference N Y
## N 3796 145
## Y 177 686
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.329725e-01 7.692476e-01 9.255302e-01 9.398832e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 1.026390e-114 8.406670e-02
## Prediction
## Reference N Y
## N 1151 347
## Y 67 163
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.604167e-01 3.148374e-01 7.395703e-01 7.803749e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 8.593187e-43
## id feats
## 1 Max.cor.Y.rcv.1X1###glmnet WordCount.root2,NDSSName.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 1.024 0.276
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8790544 0.9632073 0.7949015 0.9608594
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8099174 0.9329725
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9255302 0.9398832 0.7692476
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5962443 0.9098798 0.2826087 0.8116126
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4405405 0.7604167
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7395703 0.7803749 0.3148374# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## [1] "fitting model: Max.cor.Y.rcv.3X1##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0201 on full training set
## Length Class Mode
## a0 99 -none- numeric
## beta 2079 dgCMatrix S4
## df 99 -none- numeric
## dim 2 -none- numeric
## lambda 99 -none- numeric
## dev.ratio 99 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 21 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.89350373
## NDSSName.my.fctr#Mltmd#
## -0.01916344
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.18453357
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.21701058
## NDSSName.my.fctr#U.S.#Edctn
## -0.47679040
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.09891374
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.87281404
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.40965256
## NDSSName.my.fctrFrgn#Wrld#
## -0.05114617
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.47464340
## NDSSName.my.fctrOpEd#Opnn#
## 3.95214357
## NDSSName.my.fctrScnc#Hlth#
## 3.14232408
## NDSSName.my.fctrStyls##Fshn
## -0.31867093
## NDSSName.my.fctrStyls#U.S.#
## 2.92610567
## NDSSName.my.fctrTStyl##
## -0.60538025
## WordCount.root2
## 0.05783392
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.95644632
## NDSSName.my.fctr#Mltmd#
## -0.07182859
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.30034382
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.29694236
## NDSSName.my.fctr#U.S.#Edctn
## -0.53415905
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.14259759
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.94231812
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.45657914
## NDSSName.my.fctrFrgn#Wrld#
## -0.10021084
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.53077048
## NDSSName.my.fctrOpEd#Opnn#
## 4.01324666
## NDSSName.my.fctrScnc#Hlth#
## 3.18936803
## NDSSName.my.fctrStyls##Fshn
## -0.38069674
## NDSSName.my.fctrStyls#U.S.#
## 2.97176051
## NDSSName.my.fctrTStyl##
## -0.64837249
## WordCount.root2
## 0.05978318
## Prediction
## Reference N Y
## N 3796 145
## Y 177 686
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.329725e-01 7.692476e-01 9.255302e-01 9.398832e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 1.026390e-114 8.406670e-02
## Prediction
## Reference N Y
## N 1146 352
## Y 67 163
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.575231e-01 3.107477e-01 7.365992e-01 7.775689e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 9.066396e-44
## id feats
## 1 Max.cor.Y.rcv.3X1##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.866 0.286
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8767919 0.964476 0.7891078 0.9582555
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8099174 0.9335973
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9255302 0.9398832 0.7691678
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5962443 0.9098798 0.2826087 0.8067975
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4375839 0.7575231
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7365992 0.7775689 0.3107477
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007015493 0.02403706
## [1] "fitting model: Max.cor.Y.rcv.3X3##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0201 on full training set
## Length Class Mode
## a0 99 -none- numeric
## beta 2079 dgCMatrix S4
## df 99 -none- numeric
## dim 2 -none- numeric
## lambda 99 -none- numeric
## dev.ratio 99 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 21 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.89350373
## NDSSName.my.fctr#Mltmd#
## -0.01916344
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.18453357
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.21701058
## NDSSName.my.fctr#U.S.#Edctn
## -0.47679040
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.09891374
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.87281404
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.40965256
## NDSSName.my.fctrFrgn#Wrld#
## -0.05114617
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.47464340
## NDSSName.my.fctrOpEd#Opnn#
## 3.95214357
## NDSSName.my.fctrScnc#Hlth#
## 3.14232408
## NDSSName.my.fctrStyls##Fshn
## -0.31867093
## NDSSName.my.fctrStyls#U.S.#
## 2.92610567
## NDSSName.my.fctrTStyl##
## -0.60538025
## WordCount.root2
## 0.05783392
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.95644632
## NDSSName.my.fctr#Mltmd#
## -0.07182859
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.30034382
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.29694236
## NDSSName.my.fctr#U.S.#Edctn
## -0.53415905
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.14259759
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.94231812
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.45657914
## NDSSName.my.fctrFrgn#Wrld#
## -0.10021084
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.53077048
## NDSSName.my.fctrOpEd#Opnn#
## 4.01324666
## NDSSName.my.fctrScnc#Hlth#
## 3.18936803
## NDSSName.my.fctrStyls##Fshn
## -0.38069674
## NDSSName.my.fctrStyls#U.S.#
## 2.97176051
## NDSSName.my.fctrTStyl##
## -0.64837249
## WordCount.root2
## 0.05978318
## Prediction
## Reference N Y
## N 3796 145
## Y 177 686
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.329725e-01 7.692476e-01 9.255302e-01 9.398832e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 1.026390e-114 8.406670e-02
## Prediction
## Reference N Y
## N 1146 352
## Y 67 163
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.575231e-01 3.107477e-01 7.365992e-01 7.775689e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 9.066396e-44
## id feats
## 1 Max.cor.Y.rcv.3X3##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.855 0.273
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8767919 0.964476 0.7891078 0.9582555
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8099174 0.9333193
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9255302 0.9398832 0.7690803
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5962443 0.9098798 0.2826087 0.8067975
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4375839 0.7575231
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7365992 0.7775689 0.3107477
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005178375 0.01754365
## [1] "fitting model: Max.cor.Y.rcv.3X5##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0201 on full training set
## Length Class Mode
## a0 99 -none- numeric
## beta 2079 dgCMatrix S4
## df 99 -none- numeric
## dim 2 -none- numeric
## lambda 99 -none- numeric
## dev.ratio 99 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 21 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.89350373
## NDSSName.my.fctr#Mltmd#
## -0.01916344
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.18453357
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.21701058
## NDSSName.my.fctr#U.S.#Edctn
## -0.47679040
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.09891374
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.87281404
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.40965256
## NDSSName.my.fctrFrgn#Wrld#
## -0.05114617
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.47464340
## NDSSName.my.fctrOpEd#Opnn#
## 3.95214357
## NDSSName.my.fctrScnc#Hlth#
## 3.14232408
## NDSSName.my.fctrStyls##Fshn
## -0.31867093
## NDSSName.my.fctrStyls#U.S.#
## 2.92610567
## NDSSName.my.fctrTStyl##
## -0.60538025
## WordCount.root2
## 0.05783392
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.95644632
## NDSSName.my.fctr#Mltmd#
## -0.07182859
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.30034382
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.29694236
## NDSSName.my.fctr#U.S.#Edctn
## -0.53415905
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.14259759
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.94231812
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.45657914
## NDSSName.my.fctrFrgn#Wrld#
## -0.10021084
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.53077048
## NDSSName.my.fctrOpEd#Opnn#
## 4.01324666
## NDSSName.my.fctrScnc#Hlth#
## 3.18936803
## NDSSName.my.fctrStyls##Fshn
## -0.38069674
## NDSSName.my.fctrStyls#U.S.#
## 2.97176051
## NDSSName.my.fctrTStyl##
## -0.64837249
## WordCount.root2
## 0.05978318
## Prediction
## Reference N Y
## N 3796 145
## Y 177 686
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.329725e-01 7.692476e-01 9.255302e-01 9.398832e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 1.026390e-114 8.406670e-02
## Prediction
## Reference N Y
## N 1146 352
## Y 67 163
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.575231e-01 3.107477e-01 7.365992e-01 7.775689e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 9.066396e-44
## id feats
## 1 Max.cor.Y.rcv.3X5##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 8.311 0.277
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8767919 0.964476 0.7891078 0.9582555
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8099174 0.9332218
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9255302 0.9398832 0.7686375
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5962443 0.9098798 0.2826087 0.8067975
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4375839 0.7575231
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7365992 0.7775689 0.3107477
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005396525 0.01835474
## [1] "fitting model: Max.cor.Y.rcv.5X1##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0201 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 2100 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 21 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.81141260
## NDSSName.my.fctr#Mltmd#
## -0.68105584
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.92624537
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.40699589
## NDSSName.my.fctr#U.S.#Edctn
## -0.98291999
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.22577146
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.64343834
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.82797332
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.45317927
## NDSSName.my.fctrCltr#Arts#
## -0.17187706
## NDSSName.my.fctrFrgn#Wrld#
## -0.72035867
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.99018968
## NDSSName.my.fctrOpEd#Opnn#
## 3.81891156
## NDSSName.my.fctrScnc#Hlth#
## 3.05516080
## NDSSName.my.fctrStyls##Fshn
## -0.97651721
## NDSSName.my.fctrStyls#U.S.#
## 2.84779285
## NDSSName.my.fctrTStyl##
## -0.94109645
## NDSSName.my.fctrTrvl#Trvl#
## -0.68827560
## NDSSName.my.fctrmyOthr
## -0.84423735
## WordCount.root2
## 0.06115867
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.87108412
## NDSSName.my.fctr#Mltmd#
## -0.71588942
## NDSSName.my.fctr#Opnn#RmFrDbt
## -2.02010163
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.46715540
## NDSSName.my.fctr#U.S.#Edctn
## -1.02957582
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.22558850
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.66798026
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.89132347
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.48212450
## NDSSName.my.fctrCltr#Arts#
## -0.16733777
## NDSSName.my.fctrFrgn#Wrld#
## -0.75793881
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -1.03076807
## NDSSName.my.fctrOpEd#Opnn#
## 3.87908175
## NDSSName.my.fctrScnc#Hlth#
## 3.09788786
## NDSSName.my.fctrStyls##Fshn
## -1.02481879
## NDSSName.my.fctrStyls#U.S.#
## 2.88826078
## NDSSName.my.fctrTStyl##
## -0.97585470
## NDSSName.my.fctrTrvl#Trvl#
## -0.72668427
## NDSSName.my.fctrmyOthr
## -0.90347045
## WordCount.root2
## 0.06289698
## Prediction
## Reference N Y
## N 3800 141
## Y 179 684
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.333888e-01 7.700473e-01 9.259666e-01 9.402789e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 1.097051e-115 3.860591e-02
## Prediction
## Reference N Y
## N 1137 361
## Y 53 177
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.604167e-01 3.373693e-01 7.395703e-01 7.803749e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.935747e-51
## id feats
## 1 Max.cor.Y.rcv.5X1##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 3.432 0.271
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8784031 0.9642223 0.792584 0.9607052
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8104265 0.9331818
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9259666 0.9402789 0.7689055
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5962443 0.9098798 0.2826087 0.8114863
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4609375 0.7604167
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7395703 0.7803749 0.3373693
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008837283 0.03133449
## [1] "fitting model: Max.cor.Y.rcv.5X3##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0201 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 2100 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 21 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.81141260
## NDSSName.my.fctr#Mltmd#
## -0.68105584
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.92624537
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.40699589
## NDSSName.my.fctr#U.S.#Edctn
## -0.98291999
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.22577146
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.64343834
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.82797332
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.45317927
## NDSSName.my.fctrCltr#Arts#
## -0.17187706
## NDSSName.my.fctrFrgn#Wrld#
## -0.72035867
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.99018968
## NDSSName.my.fctrOpEd#Opnn#
## 3.81891156
## NDSSName.my.fctrScnc#Hlth#
## 3.05516080
## NDSSName.my.fctrStyls##Fshn
## -0.97651721
## NDSSName.my.fctrStyls#U.S.#
## 2.84779285
## NDSSName.my.fctrTStyl##
## -0.94109645
## NDSSName.my.fctrTrvl#Trvl#
## -0.68827560
## NDSSName.my.fctrmyOthr
## -0.84423735
## WordCount.root2
## 0.06115867
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.87108412
## NDSSName.my.fctr#Mltmd#
## -0.71588942
## NDSSName.my.fctr#Opnn#RmFrDbt
## -2.02010163
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.46715540
## NDSSName.my.fctr#U.S.#Edctn
## -1.02957582
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.22558850
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.66798026
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.89132347
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.48212450
## NDSSName.my.fctrCltr#Arts#
## -0.16733777
## NDSSName.my.fctrFrgn#Wrld#
## -0.75793881
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -1.03076807
## NDSSName.my.fctrOpEd#Opnn#
## 3.87908175
## NDSSName.my.fctrScnc#Hlth#
## 3.09788786
## NDSSName.my.fctrStyls##Fshn
## -1.02481879
## NDSSName.my.fctrStyls#U.S.#
## 2.88826078
## NDSSName.my.fctrTStyl##
## -0.97585470
## NDSSName.my.fctrTrvl#Trvl#
## -0.72668427
## NDSSName.my.fctrmyOthr
## -0.90347045
## WordCount.root2
## 0.06289698
## Prediction
## Reference N Y
## N 3800 141
## Y 179 684
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.333888e-01 7.700473e-01 9.259666e-01 9.402789e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 1.097051e-115 3.860591e-02
## Prediction
## Reference N Y
## N 1137 361
## Y 53 177
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.604167e-01 3.373693e-01 7.395703e-01 7.803749e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.935747e-51
## id feats
## 1 Max.cor.Y.rcv.5X3##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 6.452 0.277
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8784031 0.9642223 0.792584 0.9607052
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8104265 0.9333905
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9259666 0.9402789 0.7698577
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5962443 0.9098798 0.2826087 0.8114863
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4609375 0.7604167
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7395703 0.7803749 0.3373693
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.006138477 0.02161286
## [1] "fitting model: Max.cor.Y.rcv.5X5##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0201 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 2100 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 21 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.81141260
## NDSSName.my.fctr#Mltmd#
## -0.68105584
## NDSSName.my.fctr#Opnn#RmFrDbt
## -1.92624537
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.40699589
## NDSSName.my.fctr#U.S.#Edctn
## -0.98291999
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.22577146
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.64343834
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.82797332
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.45317927
## NDSSName.my.fctrCltr#Arts#
## -0.17187706
## NDSSName.my.fctrFrgn#Wrld#
## -0.72035867
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.99018968
## NDSSName.my.fctrOpEd#Opnn#
## 3.81891156
## NDSSName.my.fctrScnc#Hlth#
## 3.05516080
## NDSSName.my.fctrStyls##Fshn
## -0.97651721
## NDSSName.my.fctrStyls#U.S.#
## 2.84779285
## NDSSName.my.fctrTStyl##
## -0.94109645
## NDSSName.my.fctrTrvl#Trvl#
## -0.68827560
## NDSSName.my.fctrmyOthr
## -0.84423735
## WordCount.root2
## 0.06115867
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.87108412
## NDSSName.my.fctr#Mltmd#
## -0.71588942
## NDSSName.my.fctr#Opnn#RmFrDbt
## -2.02010163
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.46715540
## NDSSName.my.fctr#U.S.#Edctn
## -1.02957582
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.22558850
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.66798026
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.89132347
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.48212450
## NDSSName.my.fctrCltr#Arts#
## -0.16733777
## NDSSName.my.fctrFrgn#Wrld#
## -0.75793881
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -1.03076807
## NDSSName.my.fctrOpEd#Opnn#
## 3.87908175
## NDSSName.my.fctrScnc#Hlth#
## 3.09788786
## NDSSName.my.fctrStyls##Fshn
## -1.02481879
## NDSSName.my.fctrStyls#U.S.#
## 2.88826078
## NDSSName.my.fctrTStyl##
## -0.97585470
## NDSSName.my.fctrTrvl#Trvl#
## -0.72668427
## NDSSName.my.fctrmyOthr
## -0.90347045
## WordCount.root2
## 0.06289698
## Prediction
## Reference N Y
## N 3800 141
## Y 179 684
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.333888e-01 7.700473e-01 9.259666e-01 9.402789e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 1.097051e-115 3.860591e-02
## Prediction
## Reference N Y
## N 1137 361
## Y 53 177
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.604167e-01 3.373693e-01 7.395703e-01 7.803749e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.935747e-51
## id feats
## 1 Max.cor.Y.rcv.5X5##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 9.15 0.269
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8784031 0.9642223 0.792584 0.9607052
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8104265 0.9331816
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9259666 0.9402789 0.7691429
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5962443 0.9098798 0.2826087 0.8114863
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4609375 0.7604167
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7395703 0.7803749 0.3373693
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0062138 0.02210061# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
label.minor = "rpart")## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Max.cor.Y[rcv.1X1.cp.0|] 1 4 rpart
## bgn end elapsed
## 4 91.766 169.327 77.561
## 5 169.327 NA NAret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1.cp.0", type=glb_model_type, trainControl.method="none",
train.method="rpart",
tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)## [1] "fitting model: Max.cor.Y.rcv.1X1.cp.0###rpart"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"## Loading required package: rpart## Fitting cp = 0 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4804
##
## CP nsplit rel error
## 1 0.3696407879 0 1.0000000
## 2 0.0984936269 1 0.6303592
## 3 0.0857473928 2 0.5318656
## 4 0.0567786790 3 0.4461182
## 5 0.0104287370 4 0.3893395
## 6 0.0057937428 5 0.3789108
## 7 0.0034762457 7 0.3673233
## 8 0.0023174971 8 0.3638470
## 9 0.0011587486 11 0.3568946
## 10 0.0007724990 13 0.3545771
## 11 0.0005793743 16 0.3522596
## 12 0.0004213631 24 0.3476246
## 13 0.0003862495 35 0.3429896
## 14 0.0000000000 41 0.3406721
##
## Variable importance
## NDSSName.my.fctrOpEd#Opnn# NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 48 14
## NDSSName.my.fctrScnc#Hlth# NDSSName.my.fctrStyls#U.S.#
## 14 11
## WordCount.root2 NDSSName.my.fctr#Opnn#ThPblcEdtr
## 9 2
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## 1
##
## Node number 1: 4804 observations, complexity param=0.3696408
## predicted class=N expected loss=0.179642 P(node) =1
## class counts: 3941 863
## probabilities: 0.820 0.180
## left son=2 (4367 obs) right son=3 (437 obs)
## Primary splits:
## NDSSName.my.fctrOpEd#Opnn# < 0.5 to the left, improve=451.59770, (0 missing)
## NDSSName.my.fctrBsnss#Crsswrds/Gms# < 0.5 to the left, improve=112.88510, (0 missing)
## WordCount.root2 < 25.75849 to the left, improve=111.17610, (0 missing)
## NDSSName.my.fctrScnc#Hlth# < 0.5 to the left, improve= 99.35206, (0 missing)
## NDSSName.my.fctrStyls#U.S.# < 0.5 to the left, improve= 68.73272, (0 missing)
##
## Node number 2: 4367 observations, complexity param=0.09849363
## predicted class=N expected loss=0.1110602 P(node) =0.9090341
## class counts: 3882 485
## probabilities: 0.889 0.111
## left son=4 (4262 obs) right son=5 (105 obs)
## Primary splits:
## NDSSName.my.fctrBsnss#Crsswrds/Gms# < 0.5 to the left, improve=135.55130, (0 missing)
## NDSSName.my.fctrScnc#Hlth# < 0.5 to the left, improve=125.07920, (0 missing)
## WordCount.root2 < 25.75849 to the left, improve= 94.70710, (0 missing)
## NDSSName.my.fctrStyls#U.S.# < 0.5 to the left, improve= 88.56821, (0 missing)
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, improve= 18.74400, (0 missing)
##
## Node number 3: 437 observations
## predicted class=Y expected loss=0.1350114 P(node) =0.09096586
## class counts: 59 378
## probabilities: 0.135 0.865
##
## Node number 4: 4262 observations, complexity param=0.08574739
## predicted class=N expected loss=0.09150634 P(node) =0.8871774
## class counts: 3872 390
## probabilities: 0.908 0.092
## left son=8 (4114 obs) right son=9 (148 obs)
## Primary splits:
## NDSSName.my.fctrScnc#Hlth# < 0.5 to the left, improve=132.96710, (0 missing)
## NDSSName.my.fctrStyls#U.S.# < 0.5 to the left, improve= 94.69099, (0 missing)
## WordCount.root2 < 26.49528 to the left, improve= 84.07487, (0 missing)
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, improve= 19.71762, (0 missing)
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve= 10.17000, (0 missing)
##
## Node number 5: 105 observations, complexity param=0.002317497
## predicted class=Y expected loss=0.0952381 P(node) =0.02185679
## class counts: 10 95
## probabilities: 0.095 0.905
## left son=10 (12 obs) right son=11 (93 obs)
## Primary splits:
## WordCount.root2 < 18.9043 to the left, improve=6.455453, (0 missing)
##
## Node number 8: 4114 observations, complexity param=0.05677868
## predicted class=N expected loss=0.06781721 P(node) =0.8563697
## class counts: 3835 279
## probabilities: 0.932 0.068
## left son=16 (3987 obs) right son=17 (127 obs)
## Primary splits:
## NDSSName.my.fctrStyls#U.S.# < 0.5 to the left, improve=102.410700, (0 missing)
## WordCount.root2 < 25.01 to the left, improve= 47.352210, (0 missing)
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, improve= 20.930810, (0 missing)
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve= 5.249425, (0 missing)
## NDSSName.my.fctrBsnss#Tchnlgy# < 0.5 to the left, improve= 2.395935, (0 missing)
##
## Node number 9: 148 observations, complexity param=0.01042874
## predicted class=Y expected loss=0.25 P(node) =0.03080766
## class counts: 37 111
## probabilities: 0.250 0.750
## left son=18 (55 obs) right son=19 (93 obs)
## Primary splits:
## WordCount.root2 < 22.72663 to the left, improve=19.274, (0 missing)
##
## Node number 10: 12 observations
## predicted class=N expected loss=0.4166667 P(node) =0.002497918
## class counts: 7 5
## probabilities: 0.583 0.417
##
## Node number 11: 93 observations
## predicted class=Y expected loss=0.03225806 P(node) =0.01935887
## class counts: 3 90
## probabilities: 0.032 0.968
##
## Node number 16: 3987 observations, complexity param=0.005793743
## predicted class=N expected loss=0.04790569 P(node) =0.8299334
## class counts: 3796 191
## probabilities: 0.952 0.048
## left son=32 (2982 obs) right son=33 (1005 obs)
## Primary splits:
## WordCount.root2 < 25.01 to the left, improve=29.253580, (0 missing)
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, improve=21.978920, (0 missing)
## NDSSName.my.fctrBsnss#Tchnlgy# < 0.5 to the left, improve= 3.887348, (0 missing)
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve= 2.348653, (0 missing)
## NDSSName.my.fctr#U.S.#Edctn < 0.5 to the right, improve= 1.187739, (0 missing)
## Surrogate splits:
## NDSSName.my.fctr#Opnn#RmFrDbt < 0.5 to the left, agree=0.758, adj=0.042, (0 split)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the left, agree=0.752, adj=0.016, (0 split)
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, agree=0.750, adj=0.008, (0 split)
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss < 0.5 to the left, agree=0.748, adj=0.002, (0 split)
##
## Node number 17: 127 observations, complexity param=0.003476246
## predicted class=Y expected loss=0.3070866 P(node) =0.0264363
## class counts: 39 88
## probabilities: 0.307 0.693
## left son=34 (13 obs) right son=35 (114 obs)
## Primary splits:
## WordCount.root2 < 15.32846 to the left, improve=2.753047, (0 missing)
##
## Node number 18: 55 observations, complexity param=0.002317497
## predicted class=N expected loss=0.4181818 P(node) =0.01144879
## class counts: 32 23
## probabilities: 0.582 0.418
## left son=36 (9 obs) right son=37 (46 obs)
## Primary splits:
## WordCount.root2 < 19.93708 to the right, improve=0.8264383, (0 missing)
##
## Node number 19: 93 observations
## predicted class=Y expected loss=0.05376344 P(node) =0.01935887
## class counts: 5 88
## probabilities: 0.054 0.946
##
## Node number 32: 2982 observations
## predicted class=N expected loss=0.01274313 P(node) =0.6207327
## class counts: 2944 38
## probabilities: 0.987 0.013
##
## Node number 33: 1005 observations, complexity param=0.005793743
## predicted class=N expected loss=0.1522388 P(node) =0.2092007
## class counts: 852 153
## probabilities: 0.848 0.152
## left son=66 (993 obs) right son=67 (12 obs)
## Primary splits:
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, improve=14.193880, (0 missing)
## NDSSName.my.fctrCltr#Arts# < 0.5 to the left, improve= 3.669601, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve= 3.556158, (0 missing)
## WordCount.root2 < 34.19795 to the left, improve= 2.582851, (0 missing)
## NDSSName.my.fctr#Opnn#RmFrDbt < 0.5 to the right, improve= 2.031748, (0 missing)
##
## Node number 34: 13 observations
## predicted class=N expected loss=0.3846154 P(node) =0.002706078
## class counts: 8 5
## probabilities: 0.615 0.385
##
## Node number 35: 114 observations, complexity param=0.000772499
## predicted class=Y expected loss=0.2719298 P(node) =0.02373022
## class counts: 31 83
## probabilities: 0.272 0.728
## left son=70 (79 obs) right son=71 (35 obs)
## Primary splits:
## WordCount.root2 < 29.21444 to the left, improve=1.020279, (0 missing)
##
## Node number 36: 9 observations
## predicted class=N expected loss=0.2222222 P(node) =0.001873439
## class counts: 7 2
## probabilities: 0.778 0.222
##
## Node number 37: 46 observations, complexity param=0.002317497
## predicted class=N expected loss=0.4565217 P(node) =0.009575354
## class counts: 25 21
## probabilities: 0.543 0.457
## left son=74 (36 obs) right son=75 (10 obs)
## Primary splits:
## WordCount.root2 < 17.01454 to the left, improve=1.514976, (0 missing)
##
## Node number 66: 993 observations, complexity param=0.0005793743
## predicted class=N expected loss=0.143001 P(node) =0.2067027
## class counts: 851 142
## probabilities: 0.857 0.143
## left son=132 (930 obs) right son=133 (63 obs)
## Primary splits:
## NDSSName.my.fctrCltr#Arts# < 0.5 to the left, improve=4.094729, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=3.106316, (0 missing)
## WordCount.root2 < 29.5127 to the left, improve=2.722793, (0 missing)
## NDSSName.my.fctrBsnss#Tchnlgy# < 0.5 to the left, improve=1.962300, (0 missing)
## NDSSName.my.fctr#Opnn#RmFrDbt < 0.5 to the right, improve=1.793603, (0 missing)
##
## Node number 67: 12 observations
## predicted class=Y expected loss=0.08333333 P(node) =0.002497918
## class counts: 1 11
## probabilities: 0.083 0.917
##
## Node number 70: 79 observations, complexity param=0.000772499
## predicted class=Y expected loss=0.3164557 P(node) =0.01644463
## class counts: 25 54
## probabilities: 0.316 0.684
## left son=140 (25 obs) right son=141 (54 obs)
## Primary splits:
## WordCount.root2 < 27.36786 to the right, improve=0.5105485, (0 missing)
##
## Node number 71: 35 observations
## predicted class=Y expected loss=0.1714286 P(node) =0.007285595
## class counts: 6 29
## probabilities: 0.171 0.829
##
## Node number 74: 36 observations, complexity param=0.001158749
## predicted class=N expected loss=0.3888889 P(node) =0.007493755
## class counts: 22 14
## probabilities: 0.611 0.389
## left son=148 (8 obs) right son=149 (28 obs)
## Primary splits:
## WordCount.root2 < 15.74773 to the right, improve=0.3968254, (0 missing)
##
## Node number 75: 10 observations
## predicted class=Y expected loss=0.3 P(node) =0.002081599
## class counts: 3 7
## probabilities: 0.300 0.700
##
## Node number 132: 930 observations, complexity param=0.0005793743
## predicted class=N expected loss=0.1311828 P(node) =0.1935887
## class counts: 808 122
## probabilities: 0.869 0.131
## left son=264 (627 obs) right son=265 (303 obs)
## Primary splits:
## WordCount.root2 < 33.97057 to the left, improve=2.913816, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=2.586923, (0 missing)
## NDSSName.my.fctrBsnss#Tchnlgy# < 0.5 to the left, improve=2.402029, (0 missing)
## NDSSName.my.fctr#Opnn#RmFrDbt < 0.5 to the right, improve=1.513920, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk < 0.5 to the left, improve=1.276783, (0 missing)
## Surrogate splits:
## NDSSName.my.fctr#Opnn#RmFrDbt < 0.5 to the left, agree=0.719, adj=0.139, (0 split)
##
## Node number 133: 63 observations, complexity param=0.0003862495
## predicted class=N expected loss=0.3174603 P(node) =0.01311407
## class counts: 43 20
## probabilities: 0.683 0.317
## left son=266 (14 obs) right son=267 (49 obs)
## Primary splits:
## WordCount.root2 < 26.99984 to the left, improve=0.38322, (0 missing)
##
## Node number 140: 25 observations, complexity param=0.000772499
## predicted class=Y expected loss=0.4 P(node) =0.005203997
## class counts: 10 15
## probabilities: 0.400 0.600
## left son=280 (8 obs) right son=281 (17 obs)
## Primary splits:
## WordCount.root2 < 28.02674 to the left, improve=1.191176, (0 missing)
##
## Node number 141: 54 observations, complexity param=0.0003862495
## predicted class=Y expected loss=0.2777778 P(node) =0.01124063
## class counts: 15 39
## probabilities: 0.278 0.722
## left son=282 (45 obs) right son=283 (9 obs)
## Primary splits:
## WordCount.root2 < 26.55173 to the left, improve=0.6, (0 missing)
##
## Node number 148: 8 observations
## predicted class=N expected loss=0.25 P(node) =0.001665279
## class counts: 6 2
## probabilities: 0.750 0.250
##
## Node number 149: 28 observations, complexity param=0.001158749
## predicted class=N expected loss=0.4285714 P(node) =0.005828476
## class counts: 16 12
## probabilities: 0.571 0.429
## left son=298 (20 obs) right son=299 (8 obs)
## Primary splits:
## WordCount.root2 < 15.06648 to the left, improve=0.8642857, (0 missing)
##
## Node number 264: 627 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.1036683 P(node) =0.1305162
## class counts: 562 65
## probabilities: 0.896 0.104
## left son=528 (561 obs) right son=529 (66 obs)
## Primary splits:
## NDSSName.my.fctrBsnss#Tchnlgy# < 0.5 to the left, improve=2.8404170, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=1.0796950, (0 missing)
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve=1.0670160, (0 missing)
## WordCount.root2 < 29.5127 to the left, improve=0.8966879, (0 missing)
## NDSSName.my.fctr#Mltmd# < 0.5 to the right, improve=0.4399337, (0 missing)
##
## Node number 265: 303 observations, complexity param=0.0005793743
## predicted class=N expected loss=0.1881188 P(node) =0.06307244
## class counts: 246 57
## probabilities: 0.812 0.188
## left son=530 (222 obs) right son=531 (81 obs)
## Primary splits:
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk < 0.5 to the left, improve=5.4890570, (0 missing)
## WordCount.root2 < 38.17067 to the right, improve=5.0156320, (0 missing)
## NDSSName.my.fctr#Opnn#RmFrDbt < 0.5 to the right, improve=3.4510070, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=1.5155860, (0 missing)
## NDSSName.my.fctr#U.S.#Edctn < 0.5 to the right, improve=0.8078801, (0 missing)
## Surrogate splits:
## WordCount.root2 < 34.08078 to the right, agree=0.739, adj=0.025, (0 split)
##
## Node number 266: 14 observations
## predicted class=N expected loss=0.2142857 P(node) =0.002914238
## class counts: 11 3
## probabilities: 0.786 0.214
##
## Node number 267: 49 observations, complexity param=0.0003862495
## predicted class=N expected loss=0.3469388 P(node) =0.01019983
## class counts: 32 17
## probabilities: 0.653 0.347
## left son=534 (10 obs) right son=535 (39 obs)
## Primary splits:
## WordCount.root2 < 41.56249 to the right, improve=0.5425432, (0 missing)
##
## Node number 280: 8 observations
## predicted class=N expected loss=0.375 P(node) =0.001665279
## class counts: 5 3
## probabilities: 0.625 0.375
##
## Node number 281: 17 observations
## predicted class=Y expected loss=0.2941176 P(node) =0.003538718
## class counts: 5 12
## probabilities: 0.294 0.706
##
## Node number 282: 45 observations, complexity param=0.0003862495
## predicted class=Y expected loss=0.3111111 P(node) =0.009367194
## class counts: 14 31
## probabilities: 0.311 0.689
## left son=564 (23 obs) right son=565 (22 obs)
## Primary splits:
## WordCount.root2 < 21.70252 to the right, improve=0.6050944, (0 missing)
##
## Node number 283: 9 observations
## predicted class=Y expected loss=0.1111111 P(node) =0.001873439
## class counts: 1 8
## probabilities: 0.111 0.889
##
## Node number 298: 20 observations
## predicted class=N expected loss=0.35 P(node) =0.004163197
## class counts: 13 7
## probabilities: 0.650 0.350
##
## Node number 299: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.001665279
## class counts: 3 5
## probabilities: 0.375 0.625
##
## Node number 528: 561 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.08734403 P(node) =0.1167777
## class counts: 512 49
## probabilities: 0.913 0.087
## left son=1056 (281 obs) right son=1057 (280 obs)
## Primary splits:
## WordCount.root2 < 29.33428 to the left, improve=1.5853030, (0 missing)
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve=0.7645570, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=0.7250433, (0 missing)
## NDSSName.my.fctrStyls##Fshn < 0.5 to the right, improve=0.3000638, (0 missing)
## NDSSName.my.fctr#Mltmd# < 0.5 to the right, improve=0.2729836, (0 missing)
## Surrogate splits:
## NDSSName.my.fctrMtr#N.Y./Rgn# < 0.5 to the left, agree=0.560, adj=0.118, (0 split)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, agree=0.533, adj=0.064, (0 split)
## NDSSName.my.fctr#Mltmd# < 0.5 to the right, agree=0.524, adj=0.046, (0 split)
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss < 0.5 to the left, agree=0.515, adj=0.029, (0 split)
## NDSSName.my.fctrStyls##Fshn < 0.5 to the right, agree=0.512, adj=0.021, (0 split)
##
## Node number 529: 66 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.2424242 P(node) =0.01373855
## class counts: 50 16
## probabilities: 0.758 0.242
## left son=1058 (38 obs) right son=1059 (28 obs)
## Primary splits:
## WordCount.root2 < 27.86575 to the left, improve=0.6070859, (0 missing)
##
## Node number 530: 222 observations
## predicted class=N expected loss=0.1306306 P(node) =0.04621149
## class counts: 193 29
## probabilities: 0.869 0.131
##
## Node number 531: 81 observations, complexity param=0.0005793743
## predicted class=N expected loss=0.345679 P(node) =0.01686095
## class counts: 53 28
## probabilities: 0.654 0.346
## left son=1062 (15 obs) right son=1063 (66 obs)
## Primary splits:
## WordCount.root2 < 41.59766 to the right, improve=2.866218, (0 missing)
##
## Node number 534: 10 observations
## predicted class=N expected loss=0.2 P(node) =0.002081599
## class counts: 8 2
## probabilities: 0.800 0.200
##
## Node number 535: 39 observations, complexity param=0.0003862495
## predicted class=N expected loss=0.3846154 P(node) =0.008118235
## class counts: 24 15
## probabilities: 0.615 0.385
## left son=1070 (32 obs) right son=1071 (7 obs)
## Primary splits:
## WordCount.root2 < 34.23387 to the left, improve=0.595467, (0 missing)
##
## Node number 564: 23 observations, complexity param=0.0003862495
## predicted class=Y expected loss=0.3913043 P(node) =0.004787677
## class counts: 9 14
## probabilities: 0.391 0.609
## left son=1128 (7 obs) right son=1129 (16 obs)
## Primary splits:
## WordCount.root2 < 23.6326 to the left, improve=0.6529503, (0 missing)
##
## Node number 565: 22 observations
## predicted class=Y expected loss=0.2272727 P(node) =0.004579517
## class counts: 5 17
## probabilities: 0.227 0.773
##
## Node number 1056: 281 observations
## predicted class=N expected loss=0.04982206 P(node) =0.05849292
## class counts: 267 14
## probabilities: 0.950 0.050
##
## Node number 1057: 280 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.125 P(node) =0.05828476
## class counts: 245 35
## probabilities: 0.875 0.125
## left son=2114 (71 obs) right son=2115 (209 obs)
## Primary splits:
## WordCount.root2 < 32.57299 to the right, improve=0.8968765, (0 missing)
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve=0.7830739, (0 missing)
## NDSSName.my.fctrMtr#N.Y./Rgn# < 0.5 to the right, improve=0.3683673, (0 missing)
## NDSSName.my.fctr#Mltmd# < 0.5 to the right, improve=0.3578067, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=0.3021494, (0 missing)
##
## Node number 1058: 38 observations
## predicted class=N expected loss=0.1842105 P(node) =0.007910075
## class counts: 31 7
## probabilities: 0.816 0.184
##
## Node number 1059: 28 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.3214286 P(node) =0.005828476
## class counts: 19 9
## probabilities: 0.679 0.321
## left son=2118 (19 obs) right son=2119 (9 obs)
## Primary splits:
## WordCount.root2 < 28.6269 to the right, improve=1.454052, (0 missing)
##
## Node number 1062: 15 observations
## predicted class=N expected loss=0.06666667 P(node) =0.003122398
## class counts: 14 1
## probabilities: 0.933 0.067
##
## Node number 1063: 66 observations, complexity param=0.0005793743
## predicted class=N expected loss=0.4090909 P(node) =0.01373855
## class counts: 39 27
## probabilities: 0.591 0.409
## left son=2126 (25 obs) right son=2127 (41 obs)
## Primary splits:
## WordCount.root2 < 35.6581 to the left, improve=1.341286, (0 missing)
##
## Node number 1070: 32 observations
## predicted class=N expected loss=0.34375 P(node) =0.006661116
## class counts: 21 11
## probabilities: 0.656 0.344
##
## Node number 1071: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001457119
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 1128: 7 observations
## predicted class=N expected loss=0.4285714 P(node) =0.001457119
## class counts: 4 3
## probabilities: 0.571 0.429
##
## Node number 1129: 16 observations
## predicted class=Y expected loss=0.3125 P(node) =0.003330558
## class counts: 5 11
## probabilities: 0.312 0.688
##
## Node number 2114: 71 observations
## predicted class=N expected loss=0.05633803 P(node) =0.01477935
## class counts: 67 4
## probabilities: 0.944 0.056
##
## Node number 2115: 209 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.1483254 P(node) =0.04350541
## class counts: 178 31
## probabilities: 0.852 0.148
## left son=4230 (12 obs) right son=4231 (197 obs)
## Primary splits:
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve=0.5601729, (0 missing)
## NDSSName.my.fctr#Mltmd# < 0.5 to the right, improve=0.5108985, (0 missing)
## WordCount.root2 < 30.09153 to the right, improve=0.4980706, (0 missing)
## NDSSName.my.fctrMtr#N.Y./Rgn# < 0.5 to the right, improve=0.4241343, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=0.3390226, (0 missing)
##
## Node number 2118: 19 observations
## predicted class=N expected loss=0.2105263 P(node) =0.003955037
## class counts: 15 4
## probabilities: 0.789 0.211
##
## Node number 2119: 9 observations
## predicted class=Y expected loss=0.4444444 P(node) =0.001873439
## class counts: 4 5
## probabilities: 0.444 0.556
##
## Node number 2126: 25 observations
## predicted class=N expected loss=0.28 P(node) =0.005203997
## class counts: 18 7
## probabilities: 0.720 0.280
##
## Node number 2127: 41 observations, complexity param=0.0005793743
## predicted class=N expected loss=0.4878049 P(node) =0.008534555
## class counts: 21 20
## probabilities: 0.512 0.488
## left son=4254 (30 obs) right son=4255 (11 obs)
## Primary splits:
## WordCount.root2 < 36.31791 to the right, improve=0.6635625, (0 missing)
##
## Node number 4230: 12 observations
## predicted class=N expected loss=0 P(node) =0.002497918
## class counts: 12 0
## probabilities: 1.000 0.000
##
## Node number 4231: 197 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.1573604 P(node) =0.04100749
## class counts: 166 31
## probabilities: 0.843 0.157
## left son=8462 (11 obs) right son=8463 (186 obs)
## Primary splits:
## NDSSName.my.fctr#Mltmd# < 0.5 to the right, improve=0.5769882, (0 missing)
## NDSSName.my.fctrMtr#N.Y./Rgn# < 0.5 to the right, improve=0.5314217, (0 missing)
## WordCount.root2 < 30.09153 to the right, improve=0.4682049, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=0.4106319, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss < 0.5 to the right, improve=0.1814254, (0 missing)
##
## Node number 4254: 30 observations, complexity param=0.0005793743
## predicted class=N expected loss=0.4333333 P(node) =0.006244796
## class counts: 17 13
## probabilities: 0.567 0.433
## left son=8508 (7 obs) right son=8509 (23 obs)
## Primary splits:
## WordCount.root2 < 37.14159 to the left, improve=0.3979296, (0 missing)
##
## Node number 4255: 11 observations
## predicted class=Y expected loss=0.3636364 P(node) =0.002289759
## class counts: 4 7
## probabilities: 0.364 0.636
##
## Node number 8462: 11 observations
## predicted class=N expected loss=0 P(node) =0.002289759
## class counts: 11 0
## probabilities: 1.000 0.000
##
## Node number 8463: 186 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.1666667 P(node) =0.03871774
## class counts: 155 31
## probabilities: 0.833 0.167
## left son=16926 (29 obs) right son=16927 (157 obs)
## Primary splits:
## NDSSName.my.fctrMtr#N.Y./Rgn# < 0.5 to the right, improve=0.6559045, (0 missing)
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=0.4920635, (0 missing)
## WordCount.root2 < 30.09153 to the right, improve=0.3890196, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss < 0.5 to the right, improve=0.2415584, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk < 0.5 to the left, improve=0.0126479, (0 missing)
##
## Node number 8508: 7 observations
## predicted class=N expected loss=0.2857143 P(node) =0.001457119
## class counts: 5 2
## probabilities: 0.714 0.286
##
## Node number 8509: 23 observations, complexity param=0.0005793743
## predicted class=N expected loss=0.4782609 P(node) =0.004787677
## class counts: 12 11
## probabilities: 0.522 0.478
## left son=17018 (8 obs) right son=17019 (15 obs)
## Primary splits:
## WordCount.root2 < 38.57459 to the right, improve=0.2615942, (0 missing)
##
## Node number 16926: 29 observations
## predicted class=N expected loss=0.06896552 P(node) =0.006036636
## class counts: 27 2
## probabilities: 0.931 0.069
##
## Node number 16927: 157 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.1847134 P(node) =0.0326811
## class counts: 128 29
## probabilities: 0.815 0.185
## left son=33854 (18 obs) right son=33855 (139 obs)
## Primary splits:
## NDSSName.my.fctrFrgn#Wrld#AsPcfc < 0.5 to the right, improve=0.67831090, (0 missing)
## WordCount.root2 < 32.38827 to the left, improve=0.61044970, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss < 0.5 to the right, improve=0.38816480, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk < 0.5 to the right, improve=0.01539613, (0 missing)
## Surrogate splits:
## WordCount.root2 < 32.51922 to the right, agree=0.892, adj=0.056, (0 split)
##
## Node number 17018: 8 observations
## predicted class=N expected loss=0.375 P(node) =0.001665279
## class counts: 5 3
## probabilities: 0.625 0.375
##
## Node number 17019: 15 observations
## predicted class=Y expected loss=0.4666667 P(node) =0.003122398
## class counts: 7 8
## probabilities: 0.467 0.533
##
## Node number 33854: 18 observations
## predicted class=N expected loss=0.05555556 P(node) =0.003746878
## class counts: 17 1
## probabilities: 0.944 0.056
##
## Node number 33855: 139 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.2014388 P(node) =0.02893422
## class counts: 111 28
## probabilities: 0.799 0.201
## left son=67710 (102 obs) right son=67711 (37 obs)
## Primary splits:
## WordCount.root2 < 30.09153 to the right, improve=0.9266317, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss < 0.5 to the right, improve=0.5580040, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk < 0.5 to the right, improve=0.1306354, (0 missing)
##
## Node number 67710: 102 observations
## predicted class=N expected loss=0.1666667 P(node) =0.02123231
## class counts: 85 17
## probabilities: 0.833 0.167
##
## Node number 67711: 37 observations, complexity param=0.0004213631
## predicted class=N expected loss=0.2972973 P(node) =0.007701915
## class counts: 26 11
## probabilities: 0.703 0.297
## left son=135422 (30 obs) right son=135423 (7 obs)
## Primary splits:
## WordCount.root2 < 29.92488 to the left, improve=3.00231700, (0 missing)
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk < 0.5 to the left, improve=0.01303089, (0 missing)
##
## Node number 135422: 30 observations
## predicted class=N expected loss=0.2 P(node) =0.006244796
## class counts: 24 6
## probabilities: 0.800 0.200
##
## Node number 135423: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001457119
## class counts: 2 5
## probabilities: 0.286 0.714
##
## n= 4804
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4804 863 N (0.82035803 0.17964197)
## 2) NDSSName.my.fctrOpEd#Opnn#< 0.5 4367 485 N (0.88893978 0.11106022)
## 4) NDSSName.my.fctrBsnss#Crsswrds/Gms#< 0.5 4262 390 N (0.90849366 0.09150634)
## 8) NDSSName.my.fctrScnc#Hlth#< 0.5 4114 279 N (0.93218279 0.06781721)
## 16) NDSSName.my.fctrStyls#U.S.#< 0.5 3987 191 N (0.95209431 0.04790569)
## 32) WordCount.root2< 25.01 2982 38 N (0.98725687 0.01274313) *
## 33) WordCount.root2>=25.01 1005 153 N (0.84776119 0.15223881)
## 66) NDSSName.my.fctr#Opnn#ThPblcEdtr< 0.5 993 142 N (0.85699899 0.14300101)
## 132) NDSSName.my.fctrCltr#Arts#< 0.5 930 122 N (0.86881720 0.13118280)
## 264) WordCount.root2< 33.97057 627 65 N (0.89633174 0.10366826)
## 528) NDSSName.my.fctrBsnss#Tchnlgy#< 0.5 561 49 N (0.91265597 0.08734403)
## 1056) WordCount.root2< 29.33428 281 14 N (0.95017794 0.04982206) *
## 1057) WordCount.root2>=29.33428 280 35 N (0.87500000 0.12500000)
## 2114) WordCount.root2>=32.57299 71 4 N (0.94366197 0.05633803) *
## 2115) WordCount.root2< 32.57299 209 31 N (0.85167464 0.14832536)
## 4230) NDSSName.my.fctrTStyl##>=0.5 12 0 N (1.00000000 0.00000000) *
## 4231) NDSSName.my.fctrTStyl##< 0.5 197 31 N (0.84263959 0.15736041)
## 8462) NDSSName.my.fctr#Mltmd#>=0.5 11 0 N (1.00000000 0.00000000) *
## 8463) NDSSName.my.fctr#Mltmd#< 0.5 186 31 N (0.83333333 0.16666667)
## 16926) NDSSName.my.fctrMtr#N.Y./Rgn#>=0.5 29 2 N (0.93103448 0.06896552) *
## 16927) NDSSName.my.fctrMtr#N.Y./Rgn#< 0.5 157 29 N (0.81528662 0.18471338)
## 33854) NDSSName.my.fctrFrgn#Wrld#AsPcfc>=0.5 18 1 N (0.94444444 0.05555556) *
## 33855) NDSSName.my.fctrFrgn#Wrld#AsPcfc< 0.5 139 28 N (0.79856115 0.20143885)
## 67710) WordCount.root2>=30.09153 102 17 N (0.83333333 0.16666667) *
## 67711) WordCount.root2< 30.09153 37 11 N (0.70270270 0.29729730)
## 135422) WordCount.root2< 29.92488 30 6 N (0.80000000 0.20000000) *
## 135423) WordCount.root2>=29.92488 7 2 Y (0.28571429 0.71428571) *
## 529) NDSSName.my.fctrBsnss#Tchnlgy#>=0.5 66 16 N (0.75757576 0.24242424)
## 1058) WordCount.root2< 27.86575 38 7 N (0.81578947 0.18421053) *
## 1059) WordCount.root2>=27.86575 28 9 N (0.67857143 0.32142857)
## 2118) WordCount.root2>=28.6269 19 4 N (0.78947368 0.21052632) *
## 2119) WordCount.root2< 28.6269 9 4 Y (0.44444444 0.55555556) *
## 265) WordCount.root2>=33.97057 303 57 N (0.81188119 0.18811881)
## 530) NDSSName.my.fctrBsnss#BsnssDy#Dlbk< 0.5 222 29 N (0.86936937 0.13063063) *
## 531) NDSSName.my.fctrBsnss#BsnssDy#Dlbk>=0.5 81 28 N (0.65432099 0.34567901)
## 1062) WordCount.root2>=41.59766 15 1 N (0.93333333 0.06666667) *
## 1063) WordCount.root2< 41.59766 66 27 N (0.59090909 0.40909091)
## 2126) WordCount.root2< 35.6581 25 7 N (0.72000000 0.28000000) *
## 2127) WordCount.root2>=35.6581 41 20 N (0.51219512 0.48780488)
## 4254) WordCount.root2>=36.31791 30 13 N (0.56666667 0.43333333)
## 8508) WordCount.root2< 37.14159 7 2 N (0.71428571 0.28571429) *
## 8509) WordCount.root2>=37.14159 23 11 N (0.52173913 0.47826087)
## 17018) WordCount.root2>=38.57459 8 3 N (0.62500000 0.37500000) *
## 17019) WordCount.root2< 38.57459 15 7 Y (0.46666667 0.53333333) *
## 4255) WordCount.root2< 36.31791 11 4 Y (0.36363636 0.63636364) *
## 133) NDSSName.my.fctrCltr#Arts#>=0.5 63 20 N (0.68253968 0.31746032)
## 266) WordCount.root2< 26.99984 14 3 N (0.78571429 0.21428571) *
## 267) WordCount.root2>=26.99984 49 17 N (0.65306122 0.34693878)
## 534) WordCount.root2>=41.56249 10 2 N (0.80000000 0.20000000) *
## 535) WordCount.root2< 41.56249 39 15 N (0.61538462 0.38461538)
## 1070) WordCount.root2< 34.23387 32 11 N (0.65625000 0.34375000) *
## 1071) WordCount.root2>=34.23387 7 3 Y (0.42857143 0.57142857) *
## 67) NDSSName.my.fctr#Opnn#ThPblcEdtr>=0.5 12 1 Y (0.08333333 0.91666667) *
## 17) NDSSName.my.fctrStyls#U.S.#>=0.5 127 39 Y (0.30708661 0.69291339)
## 34) WordCount.root2< 15.32846 13 5 N (0.61538462 0.38461538) *
## 35) WordCount.root2>=15.32846 114 31 Y (0.27192982 0.72807018)
## 70) WordCount.root2< 29.21444 79 25 Y (0.31645570 0.68354430)
## 140) WordCount.root2>=27.36786 25 10 Y (0.40000000 0.60000000)
## 280) WordCount.root2< 28.02674 8 3 N (0.62500000 0.37500000) *
## 281) WordCount.root2>=28.02674 17 5 Y (0.29411765 0.70588235) *
## 141) WordCount.root2< 27.36786 54 15 Y (0.27777778 0.72222222)
## 282) WordCount.root2< 26.55173 45 14 Y (0.31111111 0.68888889)
## 564) WordCount.root2>=21.70252 23 9 Y (0.39130435 0.60869565)
## 1128) WordCount.root2< 23.6326 7 3 N (0.57142857 0.42857143) *
## 1129) WordCount.root2>=23.6326 16 5 Y (0.31250000 0.68750000) *
## 565) WordCount.root2< 21.70252 22 5 Y (0.22727273 0.77272727) *
## 283) WordCount.root2>=26.55173 9 1 Y (0.11111111 0.88888889) *
## 71) WordCount.root2>=29.21444 35 6 Y (0.17142857 0.82857143) *
## 9) NDSSName.my.fctrScnc#Hlth#>=0.5 148 37 Y (0.25000000 0.75000000)
## 18) WordCount.root2< 22.72663 55 23 N (0.58181818 0.41818182)
## 36) WordCount.root2>=19.93708 9 2 N (0.77777778 0.22222222) *
## 37) WordCount.root2< 19.93708 46 21 N (0.54347826 0.45652174)
## 74) WordCount.root2< 17.01454 36 14 N (0.61111111 0.38888889)
## 148) WordCount.root2>=15.74773 8 2 N (0.75000000 0.25000000) *
## 149) WordCount.root2< 15.74773 28 12 N (0.57142857 0.42857143)
## 298) WordCount.root2< 15.06648 20 7 N (0.65000000 0.35000000) *
## 299) WordCount.root2>=15.06648 8 3 Y (0.37500000 0.62500000) *
## 75) WordCount.root2>=17.01454 10 3 Y (0.30000000 0.70000000) *
## 19) WordCount.root2>=22.72663 93 5 Y (0.05376344 0.94623656) *
## 5) NDSSName.my.fctrBsnss#Crsswrds/Gms#>=0.5 105 10 Y (0.09523810 0.90476190)
## 10) WordCount.root2< 18.9043 12 5 N (0.58333333 0.41666667) *
## 11) WordCount.root2>=18.9043 93 3 Y (0.03225806 0.96774194) *
## 3) NDSSName.my.fctrOpEd#Opnn#>=0.5 437 59 Y (0.13501144 0.86498856) *
## Prediction
## Reference N Y
## N 3814 127
## Y 170 693
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.381765e-01 7.860827e-01 9.309917e-01 9.448229e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 2.798570e-127 1.480611e-02
## Prediction
## Reference N Y
## N 1180 318
## Y 84 146
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.673611e-01 2.953321e-01 7.467059e-01 7.871043e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 3.224022e-31
## id feats
## 1 Max.cor.Y.rcv.1X1.cp.0###rpart WordCount.root2,NDSSName.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.857 0.072
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8821543 0.9705658 0.7937428 0.9504198
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8235294 0.9381765
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9309917 0.9448229 0.7860827
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6174697 0.9218959 0.3130435 0.7773858
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4207493 0.7673611
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7467059 0.7871043 0.2953321#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = FALSE,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="rpart")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr"
## + Fold1.Rep1: cp=0.01043
## - Fold1.Rep1: cp=0.01043
## + Fold2.Rep1: cp=0.01043
## - Fold2.Rep1: cp=0.01043
## + Fold3.Rep1: cp=0.01043
## - Fold3.Rep1: cp=0.01043
## + Fold1.Rep2: cp=0.01043
## - Fold1.Rep2: cp=0.01043
## + Fold2.Rep2: cp=0.01043
## - Fold2.Rep2: cp=0.01043
## + Fold3.Rep2: cp=0.01043
## - Fold3.Rep2: cp=0.01043
## + Fold1.Rep3: cp=0.01043
## - Fold1.Rep3: cp=0.01043
## + Fold2.Rep3: cp=0.01043
## - Fold2.Rep3: cp=0.01043
## + Fold3.Rep3: cp=0.01043
## - Fold3.Rep3: cp=0.01043
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0104 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4804
##
## CP nsplit rel error
## 1 0.36964079 0 1.0000000
## 2 0.09849363 1 0.6303592
## 3 0.08574739 2 0.5318656
## 4 0.05677868 3 0.4461182
## 5 0.01042874 4 0.3893395
##
## Variable importance
## NDSSName.my.fctrOpEd#Opnn# NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 55 16
## NDSSName.my.fctrScnc#Hlth# NDSSName.my.fctrStyls#U.S.#
## 16 12
##
## Node number 1: 4804 observations, complexity param=0.3696408
## predicted class=N expected loss=0.179642 P(node) =1
## class counts: 3941 863
## probabilities: 0.820 0.180
## left son=2 (4367 obs) right son=3 (437 obs)
## Primary splits:
## NDSSName.my.fctrOpEd#Opnn# < 0.5 to the left, improve=451.59770, (0 missing)
## NDSSName.my.fctrBsnss#Crsswrds/Gms# < 0.5 to the left, improve=112.88510, (0 missing)
## WordCount.root2 < 25.75849 to the left, improve=111.17610, (0 missing)
## NDSSName.my.fctrScnc#Hlth# < 0.5 to the left, improve= 99.35206, (0 missing)
## NDSSName.my.fctrStyls#U.S.# < 0.5 to the left, improve= 68.73272, (0 missing)
##
## Node number 2: 4367 observations, complexity param=0.09849363
## predicted class=N expected loss=0.1110602 P(node) =0.9090341
## class counts: 3882 485
## probabilities: 0.889 0.111
## left son=4 (4262 obs) right son=5 (105 obs)
## Primary splits:
## NDSSName.my.fctrBsnss#Crsswrds/Gms# < 0.5 to the left, improve=135.55130, (0 missing)
## NDSSName.my.fctrScnc#Hlth# < 0.5 to the left, improve=125.07920, (0 missing)
## WordCount.root2 < 25.75849 to the left, improve= 94.70710, (0 missing)
## NDSSName.my.fctrStyls#U.S.# < 0.5 to the left, improve= 88.56821, (0 missing)
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, improve= 18.74400, (0 missing)
##
## Node number 3: 437 observations
## predicted class=Y expected loss=0.1350114 P(node) =0.09096586
## class counts: 59 378
## probabilities: 0.135 0.865
##
## Node number 4: 4262 observations, complexity param=0.08574739
## predicted class=N expected loss=0.09150634 P(node) =0.8871774
## class counts: 3872 390
## probabilities: 0.908 0.092
## left son=8 (4114 obs) right son=9 (148 obs)
## Primary splits:
## NDSSName.my.fctrScnc#Hlth# < 0.5 to the left, improve=132.96710, (0 missing)
## NDSSName.my.fctrStyls#U.S.# < 0.5 to the left, improve= 94.69099, (0 missing)
## WordCount.root2 < 26.49528 to the left, improve= 84.07487, (0 missing)
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, improve= 19.71762, (0 missing)
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve= 10.17000, (0 missing)
##
## Node number 5: 105 observations
## predicted class=Y expected loss=0.0952381 P(node) =0.02185679
## class counts: 10 95
## probabilities: 0.095 0.905
##
## Node number 8: 4114 observations, complexity param=0.05677868
## predicted class=N expected loss=0.06781721 P(node) =0.8563697
## class counts: 3835 279
## probabilities: 0.932 0.068
## left son=16 (3987 obs) right son=17 (127 obs)
## Primary splits:
## NDSSName.my.fctrStyls#U.S.# < 0.5 to the left, improve=102.410700, (0 missing)
## WordCount.root2 < 25.01 to the left, improve= 47.352210, (0 missing)
## NDSSName.my.fctr#Opnn#ThPblcEdtr < 0.5 to the left, improve= 20.930810, (0 missing)
## NDSSName.my.fctrTStyl## < 0.5 to the right, improve= 5.249425, (0 missing)
## NDSSName.my.fctrBsnss#Tchnlgy# < 0.5 to the left, improve= 2.395935, (0 missing)
##
## Node number 9: 148 observations
## predicted class=Y expected loss=0.25 P(node) =0.03080766
## class counts: 37 111
## probabilities: 0.250 0.750
##
## Node number 16: 3987 observations
## predicted class=N expected loss=0.04790569 P(node) =0.8299334
## class counts: 3796 191
## probabilities: 0.952 0.048
##
## Node number 17: 127 observations
## predicted class=Y expected loss=0.3070866 P(node) =0.0264363
## class counts: 39 88
## probabilities: 0.307 0.693
##
## n= 4804
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4804 863 N (0.82035803 0.17964197)
## 2) NDSSName.my.fctrOpEd#Opnn#< 0.5 4367 485 N (0.88893978 0.11106022)
## 4) NDSSName.my.fctrBsnss#Crsswrds/Gms#< 0.5 4262 390 N (0.90849366 0.09150634)
## 8) NDSSName.my.fctrScnc#Hlth#< 0.5 4114 279 N (0.93218279 0.06781721)
## 16) NDSSName.my.fctrStyls#U.S.#< 0.5 3987 191 N (0.95209431 0.04790569) *
## 17) NDSSName.my.fctrStyls#U.S.#>=0.5 127 39 Y (0.30708661 0.69291339) *
## 9) NDSSName.my.fctrScnc#Hlth#>=0.5 148 37 Y (0.25000000 0.75000000) *
## 5) NDSSName.my.fctrBsnss#Crsswrds/Gms#>=0.5 105 10 Y (0.09523810 0.90476190) *
## 3) NDSSName.my.fctrOpEd#Opnn#>=0.5 437 59 Y (0.13501144 0.86498856) *
## Prediction
## Reference N Y
## N 3796 145
## Y 191 672
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.300583e-01 7.576571e-01 9.224771e-01 9.371115e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 4.458834e-108 1.409037e-02
## Prediction
## Reference N Y
## N 1355 143
## Y 168 62
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8200231 0.1825002 0.8010821 0.8378705 0.8668981
## AccuracyPValue McnemarPValue
## 1.0000000 0.1735405
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart WordCount.root2,NDSSName.my.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.988 0.076 0.8709432
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.9632073 0.778679 0.8746354 0.6
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.8 0.9296422 0.9224771
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.9371115 0.7515134 0.5870523 0.9045394
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.2695652 0.5892132 0.6 0.2850575
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.8200231 0.8010821 0.8378705
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.1825002 0.00506952 0.0191091if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor
## 5 fit.models_0_Max.cor.Y[rcv.1X1.cp.0|] 1 4 rpart
## 6 fit.models_0_Max.cor.Y.Time.Poly 1 5 glmnet
## bgn end elapsed
## 5 169.327 183.585 14.258
## 6 183.586 NA NA
## [1] "fitting model: Max.cor.Y.Time.Poly##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.day.minutes.poly.2,PubDate.day.minutes.poly.3,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.5"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0201 on full training set
## Length Class Mode
## a0 95 -none- numeric
## beta 2470 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 26 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) NDSSName.my.fctr#Opnn#RmFrDbt
## -3.824918286 -0.730902967
## NDSSName.my.fctr#Opnn#ThPblcEdtr NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.927432404 3.640472806
## NDSSName.my.fctrBsnss#Tchnlgy# NDSSName.my.fctrFrgn#Wrld#AsPcfc
## 0.192915649 -0.002462006
## NDSSName.my.fctrOpEd#Opnn# NDSSName.my.fctrScnc#Hlth#
## 3.940210915 3.120520277
## NDSSName.my.fctrStyls#U.S.# NDSSName.my.fctrTStyl##
## 2.887354523 -0.349210176
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.2
## 10.171710001 1.938052563
## PubDate.day.minutes.poly.4 WordCount.root2
## 0.422263612 0.053515500
## [1] "max lambda < lambdaOpt:"
## (Intercept) NDSSName.my.fctr#Opnn#RmFrDbt
## -3.902999004 -0.872538727
## NDSSName.my.fctr#Opnn#ThPblcEdtr NDSSName.my.fctr#U.S.#Edctn
## 3.038605329 -0.004334849
## NDSSName.my.fctrBsnss#Crsswrds/Gms# NDSSName.my.fctrBsnss#Tchnlgy#
## 3.700053147 0.274702193
## NDSSName.my.fctrFrgn#Wrld#AsPcfc NDSSName.my.fctrOpEd#Opnn#
## -0.061833760 4.010945376
## NDSSName.my.fctrScnc#Hlth# NDSSName.my.fctrStyls#U.S.#
## 3.177975989 2.950583177
## NDSSName.my.fctrTStyl## PubDate.day.minutes.poly.1
## -0.393389148 11.082450379
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.4
## 2.849459283 1.090937007
## WordCount.root2
## 0.055710821
## Prediction
## Reference N Y
## N 3796 145
## Y 177 686
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.329725e-01 7.692476e-01 9.255302e-01 9.398832e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 1.026390e-114 8.406670e-02
## Prediction
## Reference N Y
## N 1185 313
## Y 75 155
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.754630e-01 3.233542e-01 7.550404e-01 7.949457e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 2.416777e-33
## id
## 1 Max.cor.Y.Time.Poly##rcv#glmnet
## feats
## 1 WordCount.root2,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.day.minutes.poly.2,PubDate.day.minutes.poly.3,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.944 0.325
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.874855 0.9652372 0.7844728 0.9565422
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8099174 0.932279
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9255302 0.9398832 0.7643948
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.596578 0.9105474 0.2826087 0.8049472
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4441261 0.775463
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7550404 0.7949457 0.3233542
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.00531278 0.01857466if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glmnet_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor
## 6 fit.models_0_Max.cor.Y.Time.Poly 1 5 glmnet
## 7 fit.models_0_Max.cor.Y.Time.Lag 1 6 glmnet
## bgn end elapsed
## 6 183.586 195.343 11.757
## 7 195.344 NA NA
## [1] "fitting model: Max.cor.Y.Time.Lag##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr,PubDate.last2.log1p,PubDate.last4.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.last32.log1p"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0934 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y.Time.Lag", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y.Time.Lag", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 2600 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 26 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.176434160
## NDSSName.my.fctr#Mltmd#
## -0.126861451
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.486996181
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 1.988487294
## NDSSName.my.fctr#U.S.#Edctn
## -0.358275828
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.150520660
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.142657391
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.404486307
## NDSSName.my.fctrCltr#Arts#
## -0.134298496
## NDSSName.my.fctrFrgn#Wrld#
## -0.183668961
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.337511604
## NDSSName.my.fctrOpEd#Opnn#
## 2.519630371
## NDSSName.my.fctrScnc#Hlth#
## 2.009604889
## NDSSName.my.fctrStyls##Fshn
## -0.261634932
## NDSSName.my.fctrStyls#U.S.#
## 1.848208468
## NDSSName.my.fctrTStyl##
## -0.423871057
## NDSSName.my.fctrTrvl#Trvl#
## -0.108409058
## PubDate.last2.log1p
## 0.017330994
## PubDate.last4.log1p
## 0.030509656
## PubDate.last8.log1p
## 0.006803512
## WordCount.root2
## 0.031613506
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.298280366
## NDSSName.my.fctr#Mltmd#
## -0.161556150
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.562724348
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 2.097804217
## NDSSName.my.fctr#U.S.#Edctn
## -0.392971974
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.160713783
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.173587156
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.493544311
## NDSSName.my.fctrCltr#Arts#
## -0.143142862
## NDSSName.my.fctrFrgn#Wrld#
## -0.214101718
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.377780436
## NDSSName.my.fctrOpEd#Opnn#
## 2.606990045
## NDSSName.my.fctrScnc#Hlth#
## 2.086724293
## NDSSName.my.fctrStyls##Fshn
## -0.300914741
## NDSSName.my.fctrStyls#U.S.#
## 1.924528272
## NDSSName.my.fctrTStyl##
## -0.450495150
## NDSSName.my.fctrTrvl#Trvl#
## -0.140386403
## NDSSName.my.fctrmyOthr
## -0.024631659
## PubDate.last2.log1p
## 0.019411174
## PubDate.last4.log1p
## 0.033396493
## PubDate.last8.log1p
## 0.009744315
## WordCount.root2
## 0.033177182
## Prediction
## Reference N Y
## N 3791 150
## Y 173 690
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.327644e-01 7.694835e-01 9.253120e-01 9.396854e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 3.123849e-114 2.209097e-01
## Prediction
## Reference N Y
## N 908 590
## Y 21 209
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.464120e-01 2.515036e-01 6.233476e-01 6.689781e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 7.592139e-117
## id
## 1 Max.cor.Y.Time.Lag##rcv#glmnet
## feats
## 1 WordCount.root2,NDSSName.my.fctr,PubDate.last2.log1p,PubDate.last4.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.last32.log1p
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 3.978 0.31
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.841928 0.9781781 0.7056779 0.9581739
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.8103347 0.9281861
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.925312 0.9396854 0.7362844
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5928717 0.9379172 0.2478261 0.8118709
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4062196 0.646412
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.6233476 0.6689781 0.2515036
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004492459 0.01977567# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 7 fit.models_0_Max.cor.Y.Time.Lag 1 6 glmnet
## 8 fit.models_0_Interact.High.cor.Y 1 7 glmnet
## bgn end elapsed
## 7 195.344 206.242 10.899
## 8 206.243 NA NA
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr,WordCount.root2:WordCount.root2,WordCount.root2:PubDate.day.minutes.poly.1,WordCount.root2:PubDate.last4.log1p,WordCount.root2:PubDate.last8.log1p,WordCount.root2:PubDate.month.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0201 on full training set
## Length Class Mode
## a0 93 -none- numeric
## beta 2511 dgCMatrix S4
## df 93 -none- numeric
## dim 2 -none- numeric
## lambda 93 -none- numeric
## dev.ratio 93 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 27 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.845236612
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.563614974
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 2.783870533
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.561739787
## NDSSName.my.fctrOpEd#Opnn#
## 4.006550818
## NDSSName.my.fctrScnc#Hlth#
## 3.128996320
## NDSSName.my.fctrStyls#U.S.#
## 2.876817882
## NDSSName.my.fctrTStyl##
## -0.111222771
## WordCount.root2
## 0.008932084
## WordCount.root2:PubDate.day.minutes.poly.1
## 0.394598722
## WordCount.root2:PubDate.last4.log1p
## 0.002954057
## WordCount.root2:PubDate.last8.log1p
## 0.002354682
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.926125697
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.744959921
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 2.912506461
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.630806510
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.084402547
## NDSSName.my.fctrOpEd#Opnn#
## 4.076710080
## NDSSName.my.fctrScnc#Hlth#
## 3.198327583
## NDSSName.my.fctrStyls#U.S.#
## 2.943556606
## NDSSName.my.fctrTStyl##
## -0.164715659
## WordCount.root2
## 0.009242906
## WordCount.root2:PubDate.day.minutes.poly.1
## 0.450542048
## WordCount.root2:PubDate.last4.log1p
## 0.003041430
## WordCount.root2:PubDate.last8.log1p
## 0.002496462
## Prediction
## Reference N Y
## N 3793 148
## Y 176 687
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.325562e-01 7.682400e-01 9.250938e-01 9.394875e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 9.476102e-114 1.336144e-01
## Prediction
## Reference N Y
## N 1200 298
## Y 92 138
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.743056e-01 2.908255e-01 7.538491e-01 7.938262e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 3.039278e-25
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 WordCount.root2,NDSSName.my.fctr,WordCount.root2:WordCount.root2,WordCount.root2:PubDate.day.minutes.poly.1,WordCount.root2:PubDate.last4.log1p,WordCount.root2:PubDate.last8.log1p,WordCount.root2:PubDate.month.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 6.02 0.408
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8722119 0.9657447 0.778679 0.95391
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8091873 0.931585
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9250938 0.9394875 0.7616337
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5957392 0.9132176 0.2782609 0.7992251
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.4144144 0.7743056
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7538491 0.7938262 0.2908255
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004764279 0.0162102# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 8 fit.models_0_Interact.High.cor.Y 1 7 glmnet
## 9 fit.models_0_Low.cor.X 1 8 glmnet
## bgn end elapsed
## 8 206.243 218.98 12.738
## 9 218.981 NA NAindep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv &
(exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Low.cor.X",
type=glb_model_type,
tune.df = glmnet_tune_models_df,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0934 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 5000 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 50 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.99906501
## NDSSName.my.fctr#Mltmd#
## -0.06718324
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.56229342
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 1.98953458
## NDSSName.my.fctr#U.S.#Edctn
## -0.28336630
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.14715142
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.10505001
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.26329365
## NDSSName.my.fctrCltr#Arts#
## -0.17558427
## NDSSName.my.fctrFrgn#Wrld#
## -0.16747932
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.28174745
## NDSSName.my.fctrOpEd#Opnn#
## 2.49255592
## NDSSName.my.fctrScnc#Hlth#
## 2.01908034
## NDSSName.my.fctrStyls##Fshn
## -0.23863092
## NDSSName.my.fctrStyls#U.S.#
## 1.84532884
## NDSSName.my.fctrTStyl##
## -0.43626208
## NDSSName.my.fctrTrvl#Trvl#
## -0.12317233
## PubDate.day.minutes.poly.1
## 10.52089736
## PubDate.day.minutes.poly.2
## 2.43933073
## PubDate.day.minutes.poly.4
## 3.62908242
## PubDate.last4.log1p
## 0.03007298
## PubDate.wkend
## 0.15280167
## WordCount.root2
## 0.03121091
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.08485880
## NDSSName.my.fctr#Mltmd#
## -0.09522910
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.64388705
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 2.09879679
## NDSSName.my.fctr#U.S.#Edctn
## -0.31362838
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.15761608
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.13286636
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.34188155
## NDSSName.my.fctrCltr#Arts#
## -0.18971959
## NDSSName.my.fctrFrgn#Wrld#
## -0.19646484
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.31803524
## NDSSName.my.fctrOpEd#Opnn#
## 2.57891984
## NDSSName.my.fctrScnc#Hlth#
## 2.09589193
## NDSSName.my.fctrStyls##Fshn
## -0.27680902
## NDSSName.my.fctrStyls#U.S.#
## 1.92169587
## NDSSName.my.fctrTStyl##
## -0.46355454
## NDSSName.my.fctrTrvl#Trvl#
## -0.15595755
## NDSSName.my.fctrmyOthr
## -0.02171951
## PubDate.day.minutes.poly.1
## 10.96451292
## PubDate.day.minutes.poly.2
## 2.80569325
## PubDate.day.minutes.poly.4
## 3.95458868
## PubDate.last4.log1p
## 0.03347048
## PubDate.wkend
## 0.16348293
## WordCount.root2
## 0.03275038
## Prediction
## Reference N Y
## N 3787 154
## Y 173 690
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.319317e-01 7.670549e-01 9.244394e-01 9.388938e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 2.593356e-112 3.195407e-01
## Prediction
## Reference N Y
## N 898 600
## Y 26 204
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.377315e-01 2.365595e-01 6.145608e-01 6.604334e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 4.469878e-116
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 WordCount.root2,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 5.679 0.512
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8383968 0.9769094 0.6998841 0.9582998
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.8084359 0.9261046
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9244394 0.9388938 0.7284544
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.587685 0.9405874 0.2347826 0.8150026
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.3945841 0.6377315
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.6145608 0.6604334 0.2365595
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004915343 0.02126499fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn
## 9 fit.models_0_Low.cor.X 1 8 glmnet 218.981
## 10 fit.models_0_end 1 9 teardown 231.983
## end elapsed
## 9 231.982 13.001
## 10 NA NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 10 fit.models 6 0 0 83.208 231.997 148.789
## 11 fit.models 6 1 1 231.997 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor="setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 245.758 NA NA#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glb_mdl_family_lst)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indep_vars <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjust_interaction_feats(myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
indep_vars <- setdiff(indep_vars, topindep_var)
if (length(interact_vars) > 0) {
indep_vars <-
setdiff(indep_vars, myextract_actual_feats(interact_vars))
indep_vars <- c(indep_vars,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indep_vars <- union(indep_vars, topindep_var)
}
}
if (is.null(indep_vars))
indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indep_vars))
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
if ((length(indep_vars) == 1) && (grepl("^%<d-%", indep_vars))) {
indep_vars <-
eval(parse(text = str_trim(unlist(strsplit(indep_vars, "%<d-%"))[2])))
}
indep_vars <- myadjust_interaction_feats(indep_vars)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]])) {
if (!is.null(glb_mdl_family_lst[["Best.Interact"]]))
glb_mdl_family_lst[[mdl_id_pfx]] <-
glb_mdl_family_lst[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glb_id_var] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glbObsFit
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glb_mdl_family_lst[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars <- setdiff(indep_vars, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
# The last([[:digit:]]+)(.*)\\.ctg feats are taking a long time for this experiment & nor proving to be important
indep_vars <- indep_vars[!grepl("\\.last([[:digit:]]+)(.*)\\.ctg", indep_vars)]
# The poly\\.([[:digit:]]+)\\.ctg feats are taking a long time for this experiment & nor proving to be important
indep_vars <- indep_vars[!grepl("\\.poly\\.([[:digit:]]+)\\.ctg", indep_vars)]
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df =
if ((mdl_id_pfx %in% "All.X") && (method %in% "glmnet")) glmnet_tune_models_df else
glb_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
#trainControl.allowParallel = FALSE,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 245.758 245.768
## 2 fit.models_1_All.X 1 1 setup 245.769 NA
## elapsed
## 1 0.011
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 245.769 245.776
## 3 fit.models_1_All.X 1 2 glmnet 245.776 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,WordCount.log1p,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.hour.fctr,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.last2.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,PubDate.juliandate,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0934 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 5700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 57 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.797789581
## NDSSName.my.fctr#Mltmd#
## -0.033305395
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.609594650
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 1.951072023
## NDSSName.my.fctr#U.S.#Edctn
## -0.268438616
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.165907382
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.128680248
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.226214409
## NDSSName.my.fctrCltr#Arts#
## -0.172700226
## NDSSName.my.fctrFrgn#Wrld#
## -0.142721283
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.305563165
## NDSSName.my.fctrOpEd#Opnn#
## 2.487652981
## NDSSName.my.fctrScnc#Hlth#
## 1.989726118
## NDSSName.my.fctrStyls##Fshn
## -0.252459875
## NDSSName.my.fctrStyls#U.S.#
## 1.824219364
## NDSSName.my.fctrTStyl##
## -0.420594594
## NDSSName.my.fctrTrvl#Trvl#
## -0.113478486
## PubDate.day.minutes.poly.1
## 9.907499536
## PubDate.day.minutes.poly.2
## 1.591602549
## PubDate.day.minutes.poly.4
## 4.067701888
## PubDate.hour.fctr(15.3,23]
## 0.040523622
## PubDate.last2.log1p
## 0.012602937
## PubDate.last4.log1p
## 0.022795288
## PubDate.last8.log1p
## 0.003719588
## PubDate.wkend
## 0.143132859
## WordCount.log1p
## 0.149525804
## WordCount.root2
## 0.023690424
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.930420228
## NDSSName.my.fctr#Mltmd#
## -0.060818787
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.690598861
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 2.059371027
## NDSSName.my.fctr#U.S.#Edctn
## -0.298341354
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.177530124
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.157260610
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.302913242
## NDSSName.my.fctrCltr#Arts#
## -0.186887476
## NDSSName.my.fctrFrgn#Wrld#
## -0.171078777
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.343168978
## NDSSName.my.fctrOpEd#Opnn#
## 2.575027796
## NDSSName.my.fctrScnc#Hlth#
## 2.065753009
## NDSSName.my.fctrStyls##Fshn
## -0.291299477
## NDSSName.my.fctrStyls#U.S.#
## 1.900303104
## NDSSName.my.fctrTStyl##
## -0.447367186
## NDSSName.my.fctrTrvl#Trvl#
## -0.146040451
## NDSSName.my.fctrmyOthr
## -0.021887251
## PubDate.day.minutes.poly.1
## 10.325074080
## PubDate.day.minutes.poly.2
## 1.878898682
## PubDate.day.minutes.poly.4
## 4.431162446
## PubDate.hour.fctr(15.3,23]
## 0.042785355
## PubDate.last2.log1p
## 0.014138234
## PubDate.last4.log1p
## 0.024615262
## PubDate.last8.log1p
## 0.005991056
## PubDate.wkend
## 0.151771573
## WordCount.log1p
## 0.156106145
## WordCount.root2
## 0.024698908
## Prediction
## Reference N Y
## N 3790 151
## Y 177 686
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.317236e-01 7.655936e-01 9.242213e-01 9.386958e-01 8.203580e-01
## AccuracyPValue McnemarPValue
## 7.764099e-112 1.674653e-01
## Prediction
## Reference N Y
## N 874 624
## Y 22 208
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.261574e-01 2.314268e-01 6.028573e-01 6.490282e-01 8.668981e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.296751e-123
## id
## 1 All.X##rcv#glmnet
## feats
## 1 WordCount.root2,WordCount.log1p,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.hour.fctr,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.last2.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,PubDate.juliandate,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 8.046 0.633
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8389043 0.9779244 0.6998841 0.9585414
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.8070588 0.9263124
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9242213 0.9386958 0.7277552
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.587685 0.9405874 0.2347826 0.8157921
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.3917137 0.6261574
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.6028573 0.6490282 0.2314268
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004624302 0.01929369# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 245.776 261.643
## 4 fit.models_1_preProc 1 3 preProc 261.644 NA
## elapsed
## 3 15.867
## 4 NAmdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glb_id_var] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glb_tune_models_df,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y.rcv.3X1##rcv#glmnet Max.cor.Y.rcv.3X1##rcv#glmnet
## Max.cor.Y.rcv.3X3##rcv#glmnet Max.cor.Y.rcv.3X3##rcv#glmnet
## Max.cor.Y.rcv.3X5##rcv#glmnet Max.cor.Y.rcv.3X5##rcv#glmnet
## Max.cor.Y.rcv.5X1##rcv#glmnet Max.cor.Y.rcv.5X1##rcv#glmnet
## Max.cor.Y.rcv.5X3##rcv#glmnet Max.cor.Y.rcv.5X3##rcv#glmnet
## Max.cor.Y.rcv.5X5##rcv#glmnet Max.cor.Y.rcv.5X5##rcv#glmnet
## Max.cor.Y.rcv.1X1.cp.0###rpart Max.cor.Y.rcv.1X1.cp.0###rpart
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Max.cor.Y.Time.Poly##rcv#glmnet Max.cor.Y.Time.Poly##rcv#glmnet
## Max.cor.Y.Time.Lag##rcv#glmnet Max.cor.Y.Time.Lag##rcv#glmnet
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.3X1##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.3X3##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.3X5##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.5X1##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.5X3##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.5X5##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.1X1.cp.0###rpart WordCount.root2,NDSSName.my.fctr
## Max.cor.Y##rcv#rpart WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.Time.Poly##rcv#glmnet WordCount.root2,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.day.minutes.poly.2,PubDate.day.minutes.poly.3,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.5
## Max.cor.Y.Time.Lag##rcv#glmnet WordCount.root2,NDSSName.my.fctr,PubDate.last2.log1p,PubDate.last4.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.last32.log1p
## Interact.High.cor.Y##rcv#glmnet WordCount.root2,NDSSName.my.fctr,WordCount.root2:WordCount.root2,WordCount.root2:PubDate.day.minutes.poly.1,WordCount.root2:PubDate.last4.log1p,WordCount.root2:PubDate.last8.log1p,WordCount.root2:PubDate.month.fctr
## Low.cor.X##rcv#glmnet WordCount.root2,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5
## All.X##rcv#glmnet WordCount.root2,WordCount.log1p,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.hour.fctr,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.last2.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,PubDate.juliandate,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr 0 0.296
## Random###myrandom_classfr 0 0.298
## Max.cor.Y.rcv.1X1###glmnet 0 1.024
## Max.cor.Y.rcv.3X1##rcv#glmnet 25 2.866
## Max.cor.Y.rcv.3X3##rcv#glmnet 25 4.855
## Max.cor.Y.rcv.3X5##rcv#glmnet 25 8.311
## Max.cor.Y.rcv.5X1##rcv#glmnet 25 3.432
## Max.cor.Y.rcv.5X3##rcv#glmnet 25 6.452
## Max.cor.Y.rcv.5X5##rcv#glmnet 25 9.150
## Max.cor.Y.rcv.1X1.cp.0###rpart 0 0.857
## Max.cor.Y##rcv#rpart 5 2.988
## Max.cor.Y.Time.Poly##rcv#glmnet 25 4.944
## Max.cor.Y.Time.Lag##rcv#glmnet 5 3.978
## Interact.High.cor.Y##rcv#glmnet 25 6.020
## Low.cor.X##rcv#glmnet 5 5.679
## All.X##rcv#glmnet 5 8.046
## min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr 0.003 0.5000000
## Random###myrandom_classfr 0.002 0.4990604
## Max.cor.Y.rcv.1X1###glmnet 0.276 0.8790544
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.286 0.8767919
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.273 0.8767919
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.277 0.8767919
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.271 0.8784031
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.277 0.8784031
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.269 0.8784031
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.072 0.8821543
## Max.cor.Y##rcv#rpart 0.076 0.8709432
## Max.cor.Y.Time.Poly##rcv#glmnet 0.325 0.8748550
## Max.cor.Y.Time.Lag##rcv#glmnet 0.310 0.8419280
## Interact.High.cor.Y##rcv#glmnet 0.408 0.8722119
## Low.cor.X##rcv#glmnet 0.512 0.8383968
## All.X##rcv#glmnet 0.633 0.8389043
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 1.0000000 0.0000000 0.5000000
## Random###myrandom_classfr 0.8312611 0.1668598 0.4972757
## Max.cor.Y.rcv.1X1###glmnet 0.9632073 0.7949015 0.9608594
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.9644760 0.7891078 0.9582555
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.9644760 0.7891078 0.9582555
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.9644760 0.7891078 0.9582555
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.9642223 0.7925840 0.9607052
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.9642223 0.7925840 0.9607052
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.9642223 0.7925840 0.9607052
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.9705658 0.7937428 0.9504198
## Max.cor.Y##rcv#rpart 0.9632073 0.7786790 0.8746354
## Max.cor.Y.Time.Poly##rcv#glmnet 0.9652372 0.7844728 0.9565422
## Max.cor.Y.Time.Lag##rcv#glmnet 0.9781781 0.7056779 0.9581739
## Interact.High.cor.Y##rcv#glmnet 0.9657447 0.7786790 0.9539100
## Low.cor.X##rcv#glmnet 0.9769094 0.6998841 0.9582998
## All.X##rcv#glmnet 0.9779244 0.6998841 0.9585414
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.1 0.3045703
## Random###myrandom_classfr 0.1 0.3045703
## Max.cor.Y.rcv.1X1###glmnet 0.5 0.8099174
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.4 0.8099174
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.4 0.8099174
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.4 0.8099174
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.5 0.8104265
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.5 0.8104265
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.5 0.8104265
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.4 0.8235294
## Max.cor.Y##rcv#rpart 0.6 0.8000000
## Max.cor.Y.Time.Poly##rcv#glmnet 0.4 0.8099174
## Max.cor.Y.Time.Lag##rcv#glmnet 0.3 0.8103347
## Interact.High.cor.Y##rcv#glmnet 0.4 0.8091873
## Low.cor.X##rcv#glmnet 0.3 0.8084359
## All.X##rcv#glmnet 0.3 0.8070588
## max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr 0.1796420 0.1688795
## Random###myrandom_classfr 0.1796420 0.1688795
## Max.cor.Y.rcv.1X1###glmnet 0.9329725 0.9255302
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.9335973 0.9255302
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.9333193 0.9255302
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.9332218 0.9255302
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.9331818 0.9259666
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.9333905 0.9259666
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.9331816 0.9259666
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.9381765 0.9309917
## Max.cor.Y##rcv#rpart 0.9296422 0.9224771
## Max.cor.Y.Time.Poly##rcv#glmnet 0.9322790 0.9255302
## Max.cor.Y.Time.Lag##rcv#glmnet 0.9281861 0.9253120
## Interact.High.cor.Y##rcv#glmnet 0.9315850 0.9250938
## Low.cor.X##rcv#glmnet 0.9261046 0.9244394
## All.X##rcv#glmnet 0.9263124 0.9242213
## max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr 0.1907952 0.0000000
## Random###myrandom_classfr 0.1907952 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.9398832 0.7692476
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.9398832 0.7691678
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.9398832 0.7690803
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.9398832 0.7686375
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.9402789 0.7689055
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.9402789 0.7698577
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.9402789 0.7691429
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.9448229 0.7860827
## Max.cor.Y##rcv#rpart 0.9371115 0.7515134
## Max.cor.Y.Time.Poly##rcv#glmnet 0.9398832 0.7643948
## Max.cor.Y.Time.Lag##rcv#glmnet 0.9396854 0.7362844
## Interact.High.cor.Y##rcv#glmnet 0.9394875 0.7616337
## Low.cor.X##rcv#glmnet 0.9388938 0.7284544
## All.X##rcv#glmnet 0.9386958 0.7277552
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 1.0000000 0.0000000
## Random###myrandom_classfr 0.5125675 0.8077437 0.2173913
## Max.cor.Y.rcv.1X1###glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.6174697 0.9218959 0.3130435
## Max.cor.Y##rcv#rpart 0.5870523 0.9045394 0.2695652
## Max.cor.Y.Time.Poly##rcv#glmnet 0.5965780 0.9105474 0.2826087
## Max.cor.Y.Time.Lag##rcv#glmnet 0.5928717 0.9379172 0.2478261
## Interact.High.cor.Y##rcv#glmnet 0.5957392 0.9132176 0.2782609
## Low.cor.X##rcv#glmnet 0.5876850 0.9405874 0.2347826
## All.X##rcv#glmnet 0.5876850 0.9405874 0.2347826
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.1
## Random###myrandom_classfr 0.4857956 0.1
## Max.cor.Y.rcv.1X1###glmnet 0.8116126 0.1
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.8067975 0.1
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.8067975 0.1
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.8067975 0.1
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.8114863 0.1
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.8114863 0.1
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.8114863 0.1
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.7773858 0.1
## Max.cor.Y##rcv#rpart 0.5892132 0.6
## Max.cor.Y.Time.Poly##rcv#glmnet 0.8049472 0.1
## Max.cor.Y.Time.Lag##rcv#glmnet 0.8118709 0.1
## Interact.High.cor.Y##rcv#glmnet 0.7992251 0.1
## Low.cor.X##rcv#glmnet 0.8150026 0.1
## All.X##rcv#glmnet 0.8157921 0.1
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.2349336 0.1331019
## Random###myrandom_classfr 0.2349336 0.1331019
## Max.cor.Y.rcv.1X1###glmnet 0.4405405 0.7604167
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.4375839 0.7575231
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.4375839 0.7575231
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.4375839 0.7575231
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.4609375 0.7604167
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.4609375 0.7604167
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.4609375 0.7604167
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.4207493 0.7673611
## Max.cor.Y##rcv#rpart 0.2850575 0.8200231
## Max.cor.Y.Time.Poly##rcv#glmnet 0.4441261 0.7754630
## Max.cor.Y.Time.Lag##rcv#glmnet 0.4062196 0.6464120
## Interact.High.cor.Y##rcv#glmnet 0.4144144 0.7743056
## Low.cor.X##rcv#glmnet 0.3945841 0.6377315
## All.X##rcv#glmnet 0.3917137 0.6261574
## max.AccuracyLower.OOB
## MFO###myMFO_classfr 0.1174298
## Random###myrandom_classfr 0.1174298
## Max.cor.Y.rcv.1X1###glmnet 0.7395703
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.7365992
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.7365992
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.7365992
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.7395703
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.7395703
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.7395703
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.7467059
## Max.cor.Y##rcv#rpart 0.8010821
## Max.cor.Y.Time.Poly##rcv#glmnet 0.7550404
## Max.cor.Y.Time.Lag##rcv#glmnet 0.6233476
## Interact.High.cor.Y##rcv#glmnet 0.7538491
## Low.cor.X##rcv#glmnet 0.6145608
## All.X##rcv#glmnet 0.6028573
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO###myMFO_classfr 0.1500310 0.0000000
## Random###myrandom_classfr 0.1500310 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.7803749 0.3148374
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.7775689 0.3107477
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.7775689 0.3107477
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.7775689 0.3107477
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.7803749 0.3373693
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.7803749 0.3373693
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.7803749 0.3373693
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.7871043 0.2953321
## Max.cor.Y##rcv#rpart 0.8378705 0.1825002
## Max.cor.Y.Time.Poly##rcv#glmnet 0.7949457 0.3233542
## Max.cor.Y.Time.Lag##rcv#glmnet 0.6689781 0.2515036
## Interact.High.cor.Y##rcv#glmnet 0.7938262 0.2908255
## Low.cor.X##rcv#glmnet 0.6604334 0.2365595
## All.X##rcv#glmnet 0.6490282 0.2314268
## max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr NA NA
## Random###myrandom_classfr NA NA
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.007015493 0.02403706
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.005178375 0.01754365
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.005396525 0.01835474
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.008837283 0.03133449
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.006138477 0.02161286
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.006213800 0.02210061
## Max.cor.Y.rcv.1X1.cp.0###rpart NA NA
## Max.cor.Y##rcv#rpart 0.005069520 0.01910910
## Max.cor.Y.Time.Poly##rcv#glmnet 0.005312780 0.01857466
## Max.cor.Y.Time.Lag##rcv#glmnet 0.004492459 0.01977567
## Interact.High.cor.Y##rcv#glmnet 0.004764279 0.01621020
## Low.cor.X##rcv#glmnet 0.004915343 0.02126499
## All.X##rcv#glmnet 0.004624302 0.01929369rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 261.644 261.718
## 5 fit.models_1_end 1 4 teardown 261.719 NA
## elapsed
## 4 0.074
## 5 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 11 fit.models 6 1 1 231.997 261.728 29.731
## 12 fit.models 6 2 2 261.729 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 263.455 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y.rcv.3X1##rcv#glmnet Max.cor.Y.rcv.3X1##rcv#glmnet
## Max.cor.Y.rcv.3X3##rcv#glmnet Max.cor.Y.rcv.3X3##rcv#glmnet
## Max.cor.Y.rcv.3X5##rcv#glmnet Max.cor.Y.rcv.3X5##rcv#glmnet
## Max.cor.Y.rcv.5X1##rcv#glmnet Max.cor.Y.rcv.5X1##rcv#glmnet
## Max.cor.Y.rcv.5X3##rcv#glmnet Max.cor.Y.rcv.5X3##rcv#glmnet
## Max.cor.Y.rcv.5X5##rcv#glmnet Max.cor.Y.rcv.5X5##rcv#glmnet
## Max.cor.Y.rcv.1X1.cp.0###rpart Max.cor.Y.rcv.1X1.cp.0###rpart
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Max.cor.Y.Time.Poly##rcv#glmnet Max.cor.Y.Time.Poly##rcv#glmnet
## Max.cor.Y.Time.Lag##rcv#glmnet Max.cor.Y.Time.Lag##rcv#glmnet
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.3X1##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.3X3##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.3X5##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.5X1##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.5X3##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.5X5##rcv#glmnet WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.rcv.1X1.cp.0###rpart WordCount.root2,NDSSName.my.fctr
## Max.cor.Y##rcv#rpart WordCount.root2,NDSSName.my.fctr
## Max.cor.Y.Time.Poly##rcv#glmnet WordCount.root2,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.day.minutes.poly.2,PubDate.day.minutes.poly.3,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.5
## Max.cor.Y.Time.Lag##rcv#glmnet WordCount.root2,NDSSName.my.fctr,PubDate.last2.log1p,PubDate.last4.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.last32.log1p
## Interact.High.cor.Y##rcv#glmnet WordCount.root2,NDSSName.my.fctr,WordCount.root2:WordCount.root2,WordCount.root2:PubDate.day.minutes.poly.1,WordCount.root2:PubDate.last4.log1p,WordCount.root2:PubDate.last8.log1p,WordCount.root2:PubDate.month.fctr
## Low.cor.X##rcv#glmnet WordCount.root2,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5
## All.X##rcv#glmnet WordCount.root2,WordCount.log1p,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.hour.fctr,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.last2.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,PubDate.juliandate,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5
## max.nTuningRuns max.AUCpROC.fit
## MFO###myMFO_classfr 0 0.5000000
## Random###myrandom_classfr 0 0.4990604
## Max.cor.Y.rcv.1X1###glmnet 0 0.8790544
## Max.cor.Y.rcv.3X1##rcv#glmnet 25 0.8767919
## Max.cor.Y.rcv.3X3##rcv#glmnet 25 0.8767919
## Max.cor.Y.rcv.3X5##rcv#glmnet 25 0.8767919
## Max.cor.Y.rcv.5X1##rcv#glmnet 25 0.8784031
## Max.cor.Y.rcv.5X3##rcv#glmnet 25 0.8784031
## Max.cor.Y.rcv.5X5##rcv#glmnet 25 0.8784031
## Max.cor.Y.rcv.1X1.cp.0###rpart 0 0.8821543
## Max.cor.Y##rcv#rpart 5 0.8709432
## Max.cor.Y.Time.Poly##rcv#glmnet 25 0.8748550
## Max.cor.Y.Time.Lag##rcv#glmnet 5 0.8419280
## Interact.High.cor.Y##rcv#glmnet 25 0.8722119
## Low.cor.X##rcv#glmnet 5 0.8383968
## All.X##rcv#glmnet 5 0.8389043
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 1.0000000 0.0000000 0.5000000
## Random###myrandom_classfr 0.8312611 0.1668598 0.4972757
## Max.cor.Y.rcv.1X1###glmnet 0.9632073 0.7949015 0.9608594
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.9644760 0.7891078 0.9582555
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.9644760 0.7891078 0.9582555
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.9644760 0.7891078 0.9582555
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.9642223 0.7925840 0.9607052
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.9642223 0.7925840 0.9607052
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.9642223 0.7925840 0.9607052
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.9705658 0.7937428 0.9504198
## Max.cor.Y##rcv#rpart 0.9632073 0.7786790 0.8746354
## Max.cor.Y.Time.Poly##rcv#glmnet 0.9652372 0.7844728 0.9565422
## Max.cor.Y.Time.Lag##rcv#glmnet 0.9781781 0.7056779 0.9581739
## Interact.High.cor.Y##rcv#glmnet 0.9657447 0.7786790 0.9539100
## Low.cor.X##rcv#glmnet 0.9769094 0.6998841 0.9582998
## All.X##rcv#glmnet 0.9779244 0.6998841 0.9585414
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.1 0.3045703
## Random###myrandom_classfr 0.1 0.3045703
## Max.cor.Y.rcv.1X1###glmnet 0.5 0.8099174
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.4 0.8099174
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.4 0.8099174
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.4 0.8099174
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.5 0.8104265
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.5 0.8104265
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.5 0.8104265
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.4 0.8235294
## Max.cor.Y##rcv#rpart 0.6 0.8000000
## Max.cor.Y.Time.Poly##rcv#glmnet 0.4 0.8099174
## Max.cor.Y.Time.Lag##rcv#glmnet 0.3 0.8103347
## Interact.High.cor.Y##rcv#glmnet 0.4 0.8091873
## Low.cor.X##rcv#glmnet 0.3 0.8084359
## All.X##rcv#glmnet 0.3 0.8070588
## max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr 0.1796420 0.0000000
## Random###myrandom_classfr 0.1796420 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.9329725 0.7692476
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.9335973 0.7691678
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.9333193 0.7690803
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.9332218 0.7686375
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.9331818 0.7689055
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.9333905 0.7698577
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.9331816 0.7691429
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.9381765 0.7860827
## Max.cor.Y##rcv#rpart 0.9296422 0.7515134
## Max.cor.Y.Time.Poly##rcv#glmnet 0.9322790 0.7643948
## Max.cor.Y.Time.Lag##rcv#glmnet 0.9281861 0.7362844
## Interact.High.cor.Y##rcv#glmnet 0.9315850 0.7616337
## Low.cor.X##rcv#glmnet 0.9261046 0.7284544
## All.X##rcv#glmnet 0.9263124 0.7277552
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 1.0000000 0.0000000
## Random###myrandom_classfr 0.5125675 0.8077437 0.2173913
## Max.cor.Y.rcv.1X1###glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.5962443 0.9098798 0.2826087
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.6174697 0.9218959 0.3130435
## Max.cor.Y##rcv#rpart 0.5870523 0.9045394 0.2695652
## Max.cor.Y.Time.Poly##rcv#glmnet 0.5965780 0.9105474 0.2826087
## Max.cor.Y.Time.Lag##rcv#glmnet 0.5928717 0.9379172 0.2478261
## Interact.High.cor.Y##rcv#glmnet 0.5957392 0.9132176 0.2782609
## Low.cor.X##rcv#glmnet 0.5876850 0.9405874 0.2347826
## All.X##rcv#glmnet 0.5876850 0.9405874 0.2347826
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.1
## Random###myrandom_classfr 0.4857956 0.1
## Max.cor.Y.rcv.1X1###glmnet 0.8116126 0.1
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.8067975 0.1
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.8067975 0.1
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.8067975 0.1
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.8114863 0.1
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.8114863 0.1
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.8114863 0.1
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.7773858 0.1
## Max.cor.Y##rcv#rpart 0.5892132 0.6
## Max.cor.Y.Time.Poly##rcv#glmnet 0.8049472 0.1
## Max.cor.Y.Time.Lag##rcv#glmnet 0.8118709 0.1
## Interact.High.cor.Y##rcv#glmnet 0.7992251 0.1
## Low.cor.X##rcv#glmnet 0.8150026 0.1
## All.X##rcv#glmnet 0.8157921 0.1
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.2349336 0.1331019
## Random###myrandom_classfr 0.2349336 0.1331019
## Max.cor.Y.rcv.1X1###glmnet 0.4405405 0.7604167
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.4375839 0.7575231
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.4375839 0.7575231
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.4375839 0.7575231
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.4609375 0.7604167
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.4609375 0.7604167
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.4609375 0.7604167
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.4207493 0.7673611
## Max.cor.Y##rcv#rpart 0.2850575 0.8200231
## Max.cor.Y.Time.Poly##rcv#glmnet 0.4441261 0.7754630
## Max.cor.Y.Time.Lag##rcv#glmnet 0.4062196 0.6464120
## Interact.High.cor.Y##rcv#glmnet 0.4144144 0.7743056
## Low.cor.X##rcv#glmnet 0.3945841 0.6377315
## All.X##rcv#glmnet 0.3917137 0.6261574
## max.Kappa.OOB inv.elapsedtime.everything
## MFO###myMFO_classfr 0.0000000 3.3783784
## Random###myrandom_classfr 0.0000000 3.3557047
## Max.cor.Y.rcv.1X1###glmnet 0.3148374 0.9765625
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.3107477 0.3489184
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.3107477 0.2059732
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.3107477 0.1203225
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.3373693 0.2913753
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.3373693 0.1549907
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.3373693 0.1092896
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.2953321 1.1668611
## Max.cor.Y##rcv#rpart 0.1825002 0.3346720
## Max.cor.Y.Time.Poly##rcv#glmnet 0.3233542 0.2022654
## Max.cor.Y.Time.Lag##rcv#glmnet 0.2515036 0.2513826
## Interact.High.cor.Y##rcv#glmnet 0.2908255 0.1661130
## Low.cor.X##rcv#glmnet 0.2365595 0.1760873
## All.X##rcv#glmnet 0.2314268 0.1242854
## inv.elapsedtime.final
## MFO###myMFO_classfr 333.333333
## Random###myrandom_classfr 500.000000
## Max.cor.Y.rcv.1X1###glmnet 3.623188
## Max.cor.Y.rcv.3X1##rcv#glmnet 3.496503
## Max.cor.Y.rcv.3X3##rcv#glmnet 3.663004
## Max.cor.Y.rcv.3X5##rcv#glmnet 3.610108
## Max.cor.Y.rcv.5X1##rcv#glmnet 3.690037
## Max.cor.Y.rcv.5X3##rcv#glmnet 3.610108
## Max.cor.Y.rcv.5X5##rcv#glmnet 3.717472
## Max.cor.Y.rcv.1X1.cp.0###rpart 13.888889
## Max.cor.Y##rcv#rpart 13.157895
## Max.cor.Y.Time.Poly##rcv#glmnet 3.076923
## Max.cor.Y.Time.Lag##rcv#glmnet 3.225806
## Interact.High.cor.Y##rcv#glmnet 2.450980
## Low.cor.X##rcv#glmnet 1.953125
## All.X##rcv#glmnet 1.579779print(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 16. Consider specifying shapes manually if you must have them.## Warning: Removed 200 rows containing missing values (geom_point).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 16. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Max.cor.Y.Time.Poly##rcv#glmnet Max.cor.Y.Time.Poly##rcv#glmnet
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Max.cor.Y.rcv.1X1.cp.0###rpart Max.cor.Y.rcv.1X1.cp.0###rpart
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y.rcv.5X3##rcv#glmnet Max.cor.Y.rcv.5X3##rcv#glmnet
## Max.cor.Y.rcv.5X1##rcv#glmnet Max.cor.Y.rcv.5X1##rcv#glmnet
## Max.cor.Y.rcv.5X5##rcv#glmnet Max.cor.Y.rcv.5X5##rcv#glmnet
## Max.cor.Y.rcv.3X1##rcv#glmnet Max.cor.Y.rcv.3X1##rcv#glmnet
## Max.cor.Y.rcv.3X3##rcv#glmnet Max.cor.Y.rcv.3X3##rcv#glmnet
## Max.cor.Y.rcv.3X5##rcv#glmnet Max.cor.Y.rcv.3X5##rcv#glmnet
## Max.cor.Y.Time.Lag##rcv#glmnet Max.cor.Y.Time.Lag##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## max.Accuracy.OOB max.AUCROCR.OOB
## Max.cor.Y##rcv#rpart 0.8200231 0.5892132
## Max.cor.Y.Time.Poly##rcv#glmnet 0.7754630 0.8049472
## Interact.High.cor.Y##rcv#glmnet 0.7743056 0.7992251
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.7673611 0.7773858
## Max.cor.Y.rcv.1X1###glmnet 0.7604167 0.8116126
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.7604167 0.8114863
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.7604167 0.8114863
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.7604167 0.8114863
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.7575231 0.8067975
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.7575231 0.8067975
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.7575231 0.8067975
## Max.cor.Y.Time.Lag##rcv#glmnet 0.6464120 0.8118709
## Low.cor.X##rcv#glmnet 0.6377315 0.8150026
## All.X##rcv#glmnet 0.6261574 0.8157921
## MFO###myMFO_classfr 0.1331019 0.5000000
## Random###myrandom_classfr 0.1331019 0.4857956
## max.AUCpROC.OOB max.Accuracy.fit
## Max.cor.Y##rcv#rpart 0.5870523 0.9296422
## Max.cor.Y.Time.Poly##rcv#glmnet 0.5965780 0.9322790
## Interact.High.cor.Y##rcv#glmnet 0.5957392 0.9315850
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.6174697 0.9381765
## Max.cor.Y.rcv.1X1###glmnet 0.5962443 0.9329725
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.5962443 0.9333905
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.5962443 0.9331818
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.5962443 0.9331816
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.5962443 0.9335973
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.5962443 0.9333193
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.5962443 0.9332218
## Max.cor.Y.Time.Lag##rcv#glmnet 0.5928717 0.9281861
## Low.cor.X##rcv#glmnet 0.5876850 0.9261046
## All.X##rcv#glmnet 0.5876850 0.9263124
## MFO###myMFO_classfr 0.5000000 0.1796420
## Random###myrandom_classfr 0.5125675 0.1796420
## opt.prob.threshold.fit
## Max.cor.Y##rcv#rpart 0.6
## Max.cor.Y.Time.Poly##rcv#glmnet 0.4
## Interact.High.cor.Y##rcv#glmnet 0.4
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.4
## Max.cor.Y.rcv.1X1###glmnet 0.5
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.5
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.5
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.5
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.4
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.4
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.4
## Max.cor.Y.Time.Lag##rcv#glmnet 0.3
## Low.cor.X##rcv#glmnet 0.3
## All.X##rcv#glmnet 0.3
## MFO###myMFO_classfr 0.1
## Random###myrandom_classfr 0.1
## opt.prob.threshold.OOB
## Max.cor.Y##rcv#rpart 0.6
## Max.cor.Y.Time.Poly##rcv#glmnet 0.1
## Interact.High.cor.Y##rcv#glmnet 0.1
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.1
## Max.cor.Y.rcv.1X1###glmnet 0.1
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.1
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.1
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.1
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.1
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.1
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.1
## Max.cor.Y.Time.Lag##rcv#glmnet 0.1
## Low.cor.X##rcv#glmnet 0.1
## All.X##rcv#glmnet 0.1
## MFO###myMFO_classfr 0.1
## Random###myrandom_classfr 0.1print(myplot_radar(radar_inp_df = dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 16. Consider specifying shapes manually if you must have them.## Warning: Removed 70 rows containing missing values (geom_point).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 16. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7f8ff66ebce0>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: Max.cor.Y##rcv#rpart"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
df[, paste0(predct_var_name, ".prob")] <-
predict(mdl, newdata = df, type = "prob")
stop("Multinomial prediction error calculation needs to be implemented...")
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indep_vars <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indep_vars <- paste(indep_vars, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indep_vars <- intersect(indep_vars, names(glbObsFit))
# indep_vars <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
# if (glb_is_classification && glb_is_binomial)
# indep_vars <- grep("prob$", indep_vars, value=TRUE) else
# indep_vars <- indep_vars[!grepl("err$", indep_vars)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indep_vars)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 100 -none- numeric
## beta 5700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 57 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.797789581
## NDSSName.my.fctr#Mltmd#
## -0.033305395
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.609594650
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 1.951072023
## NDSSName.my.fctr#U.S.#Edctn
## -0.268438616
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.165907382
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.128680248
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.226214409
## NDSSName.my.fctrCltr#Arts#
## -0.172700226
## NDSSName.my.fctrFrgn#Wrld#
## -0.142721283
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.305563165
## NDSSName.my.fctrOpEd#Opnn#
## 2.487652981
## NDSSName.my.fctrScnc#Hlth#
## 1.989726118
## NDSSName.my.fctrStyls##Fshn
## -0.252459875
## NDSSName.my.fctrStyls#U.S.#
## 1.824219364
## NDSSName.my.fctrTStyl##
## -0.420594594
## NDSSName.my.fctrTrvl#Trvl#
## -0.113478486
## PubDate.day.minutes.poly.1
## 9.907499536
## PubDate.day.minutes.poly.2
## 1.591602549
## PubDate.day.minutes.poly.4
## 4.067701888
## PubDate.hour.fctr(15.3,23]
## 0.040523622
## PubDate.last2.log1p
## 0.012602937
## PubDate.last4.log1p
## 0.022795288
## PubDate.last8.log1p
## 0.003719588
## PubDate.wkend
## 0.143132859
## WordCount.log1p
## 0.149525804
## WordCount.root2
## 0.023690424
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.930420228
## NDSSName.my.fctr#Mltmd#
## -0.060818787
## NDSSName.my.fctr#Opnn#RmFrDbt
## -0.690598861
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 2.059371027
## NDSSName.my.fctr#U.S.#Edctn
## -0.298341354
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.177530124
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.157260610
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 2.302913242
## NDSSName.my.fctrCltr#Arts#
## -0.186887476
## NDSSName.my.fctrFrgn#Wrld#
## -0.171078777
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -0.343168978
## NDSSName.my.fctrOpEd#Opnn#
## 2.575027796
## NDSSName.my.fctrScnc#Hlth#
## 2.065753009
## NDSSName.my.fctrStyls##Fshn
## -0.291299477
## NDSSName.my.fctrStyls#U.S.#
## 1.900303104
## NDSSName.my.fctrTStyl##
## -0.447367186
## NDSSName.my.fctrTrvl#Trvl#
## -0.146040451
## NDSSName.my.fctrmyOthr
## -0.021887251
## PubDate.day.minutes.poly.1
## 10.325074080
## PubDate.day.minutes.poly.2
## 1.878898682
## PubDate.day.minutes.poly.4
## 4.431162446
## PubDate.hour.fctr(15.3,23]
## 0.042785355
## PubDate.last2.log1p
## 0.014138234
## PubDate.last4.log1p
## 0.024615262
## PubDate.last8.log1p
## 0.005991056
## PubDate.wkend
## 0.151771573
## WordCount.log1p
## 0.156106145
## WordCount.root2
## 0.024698908## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".imp")] <- glb_featsimp_df$imp
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
print(glb_featsimp_df)## imp All.X##rcv#glmnet.imp
## PubDate.day.minutes.poly.1 100.000000 100.000000
## PubDate.day.minutes.poly.4 46.118345 46.118345
## NDSSName.my.fctrOpEd#Opnn# 29.608813 29.608813
## NDSSName.my.fctrBsnss#Crsswrds/Gms# 27.135657 27.135657
## NDSSName.my.fctrScnc#Hlth# 24.964890 24.964890
## NDSSName.my.fctr#Opnn#ThPblcEdtr 24.849247 24.849247
## NDSSName.my.fctrStyls#U.S.# 23.449567 23.449567
## PubDate.day.minutes.poly.2 22.879214 22.879214
## WordCount.log1p 7.599061 7.599061
## PubDate.wkend 7.555716 7.555716
## PubDate.hour.fctr(15.3,23] 6.568901 6.568901
## WordCount.root2 6.405483 6.405483
## PubDate.last4.log1p 6.403279 6.403279
## PubDate.last2.log1p 6.307833 6.307833
## PubDate.last8.log1p 6.231915 6.231915
## .rnorm 6.181073 6.181073
## NDSSName.my.fctrBsnss#Tchnlgy# 6.181073 6.181073
## NDSSName.my.fctrCltr## 6.181073 6.181073
## NDSSName.my.fctrMtr#N.Y./Rgn# 6.181073 6.181073
## PubDate.date.fctr(7,13] 6.181073 6.181073
## PubDate.date.fctr(13,19] 6.181073 6.181073
## PubDate.date.fctr(19,25] 6.181073 6.181073
## PubDate.date.fctr(25,31] 6.181073 6.181073
## PubDate.day.minutes.poly.3 6.181073 6.181073
## PubDate.day.minutes.poly.5 6.181073 6.181073
## PubDate.hour.fctr(7.67,15.3] 6.181073 6.181073
## PubDate.juliandate 6.181073 6.181073
## PubDate.last16.log1p 6.181073 6.181073
## PubDate.last32.log1p 6.181073 6.181073
## PubDate.minute.fctr(14.8,29.5] 6.181073 6.181073
## PubDate.minute.fctr(29.5,44.2] 6.181073 6.181073
## PubDate.minute.fctr(44.2,59.1] 6.181073 6.181073
## PubDate.month.fctr10 6.181073 6.181073
## PubDate.month.fctr11 6.181073 6.181073
## PubDate.month.fctr12 6.181073 6.181073
## PubDate.second.fctr(14.8,29.5] 6.181073 6.181073
## PubDate.second.fctr(29.5,44.2] 6.181073 6.181073
## PubDate.second.fctr(44.2,59.1] 6.181073 6.181073
## PubDate.wkday.fctr1 6.181073 6.181073
## PubDate.wkday.fctr2 6.181073 6.181073
## PubDate.wkday.fctr3 6.181073 6.181073
## PubDate.wkday.fctr4 6.181073 6.181073
## PubDate.wkday.fctr5 6.181073 6.181073
## PubDate.wkday.fctr6 6.181073 6.181073
## WordCount.nexp 6.181073 6.181073
## NDSSName.my.fctrmyOthr 6.019416 6.019416
## NDSSName.my.fctr#Mltmd# 5.672845 5.672845
## NDSSName.my.fctrTrvl#Trvl# 4.901316 4.901316
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss 4.791503 4.791503
## NDSSName.my.fctrFrgn#Wrld# 4.664559 4.664559
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk 4.575817 4.575817
## NDSSName.my.fctrCltr#Arts# 4.494666 4.494666
## NDSSName.my.fctrStyls##Fshn 3.582132 3.582132
## NDSSName.my.fctr#U.S.#Edctn 3.501802 3.501802
## NDSSName.my.fctrFrgn#Wrld#AsPcfc 3.104915 3.104915
## NDSSName.my.fctrTStyl## 2.131448 2.131448
## NDSSName.my.fctr#Opnn#RmFrDbt 0.000000 0.000000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glb_id_var,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 23
## [1] "Min/Max Boundaries: "
## UniqueID Pplr.fctr Pplr.fctr.All.X..rcv.glmnet.prob
## 1 3918 N 0.03938738
## 2 2555 N 0.02746354
## 3 302 N 0.24364717
## Pplr.fctr.All.X..rcv.glmnet Pplr.fctr.All.X..rcv.glmnet.err
## 1 N FALSE
## 2 N FALSE
## 3 Y TRUE
## Pplr.fctr.All.X..rcv.glmnet.err.abs Pplr.fctr.All.X..rcv.glmnet.is.acc
## 1 0.03938738 TRUE
## 2 0.02746354 TRUE
## 3 0.24364717 FALSE
## Pplr.fctr.All.X..rcv.glmnet.accurate Pplr.fctr.All.X..rcv.glmnet.error
## 1 TRUE 0.0000000
## 2 TRUE 0.0000000
## 3 FALSE 0.1436472
## .label
## 1 3918
## 2 2555
## 3 302
## [1] "Inaccurate: "
## UniqueID Pplr.fctr Pplr.fctr.All.X..rcv.glmnet.prob
## 1 172 Y 0.06329231
## 2 3554 Y 0.06519608
## 3 92 Y 0.06728628
## 4 3076 Y 0.07036009
## 5 6354 Y 0.07093837
## 6 4020 Y 0.07165963
## Pplr.fctr.All.X..rcv.glmnet Pplr.fctr.All.X..rcv.glmnet.err
## 1 N TRUE
## 2 N TRUE
## 3 N TRUE
## 4 N TRUE
## 5 N TRUE
## 6 N TRUE
## Pplr.fctr.All.X..rcv.glmnet.err.abs Pplr.fctr.All.X..rcv.glmnet.is.acc
## 1 0.9367077 FALSE
## 2 0.9348039 FALSE
## 3 0.9327137 FALSE
## 4 0.9296399 FALSE
## 5 0.9290616 FALSE
## 6 0.9283404 FALSE
## Pplr.fctr.All.X..rcv.glmnet.accurate Pplr.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.03670769
## 2 FALSE -0.03480392
## 3 FALSE -0.03271372
## 4 FALSE -0.02963991
## 5 FALSE -0.02906163
## 6 FALSE -0.02834037
## UniqueID Pplr.fctr Pplr.fctr.All.X..rcv.glmnet.prob
## 156 1774 N 0.1120934
## 332 4664 N 0.1360889
## 361 6480 N 0.1410233
## 396 508 N 0.1509912
## 400 1922 N 0.1520510
## 635 483 N 0.7423412
## Pplr.fctr.All.X..rcv.glmnet Pplr.fctr.All.X..rcv.glmnet.err
## 156 Y TRUE
## 332 Y TRUE
## 361 Y TRUE
## 396 Y TRUE
## 400 Y TRUE
## 635 Y TRUE
## Pplr.fctr.All.X..rcv.glmnet.err.abs Pplr.fctr.All.X..rcv.glmnet.is.acc
## 156 0.1120934 FALSE
## 332 0.1360889 FALSE
## 361 0.1410233 FALSE
## 396 0.1509912 FALSE
## 400 0.1520510 FALSE
## 635 0.7423412 FALSE
## Pplr.fctr.All.X..rcv.glmnet.accurate Pplr.fctr.All.X..rcv.glmnet.error
## 156 FALSE 0.01209336
## 332 FALSE 0.03608890
## 361 FALSE 0.04102331
## 396 FALSE 0.05099123
## 400 FALSE 0.05205101
## 635 FALSE 0.64234122
## UniqueID Pplr.fctr Pplr.fctr.All.X..rcv.glmnet.prob
## 641 770 N 0.7842764
## 642 221 N 0.7905685
## 643 472 N 0.7907410
## 644 1448 N 0.8011158
## 645 3590 N 0.8019486
## 646 2995 N 0.8069664
## Pplr.fctr.All.X..rcv.glmnet Pplr.fctr.All.X..rcv.glmnet.err
## 641 Y TRUE
## 642 Y TRUE
## 643 Y TRUE
## 644 Y TRUE
## 645 Y TRUE
## 646 Y TRUE
## Pplr.fctr.All.X..rcv.glmnet.err.abs Pplr.fctr.All.X..rcv.glmnet.is.acc
## 641 0.7842764 FALSE
## 642 0.7905685 FALSE
## 643 0.7907410 FALSE
## 644 0.8011158 FALSE
## 645 0.8019486 FALSE
## 646 0.8069664 FALSE
## Pplr.fctr.All.X..rcv.glmnet.accurate Pplr.fctr.All.X..rcv.glmnet.error
## 641 FALSE 0.6842764
## 642 FALSE 0.6905685
## 643 FALSE 0.6907410
## 644 FALSE 0.7011158
## 645 FALSE 0.7019486
## 646 FALSE 0.7069664
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glb_sel_mdl_id,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## NDSSName.my.fctr .n.OOB .n.Fit .n.Tst
## OpEd#Opnn# OpEd#Opnn# 89 437 164
## #Opnn#ThPblcEdtr #Opnn#ThPblcEdtr 4 16 10
## Styls#U.S.# Styls#U.S.# 50 127 61
## Bsnss#Crsswrds/Gms# Bsnss#Crsswrds/Gms# 18 105 42
## Scnc#Hlth# Scnc#Hlth# 48 148 57
## Bsnss#Tchnlgy# Bsnss#Tchnlgy# 126 213 114
## ## ## 371 913 342
## Bsnss#BsnssDy#Dlbk Bsnss#BsnssDy#Dlbk 323 629 304
## Mtr#N.Y./Rgn# Mtr#N.Y./Rgn# 70 128 67
## Cltr#Arts# Cltr#Arts# 185 490 174
## #Opnn#RmFrDbt #Opnn#RmFrDbt 20 42 20
## Styls##Fshn Styls##Fshn 15 104 15
## Bsnss#BsnssDy#SmllBsnss Bsnss#BsnssDy#SmllBsnss 40 100 41
## myOthr myOthr 5 33 5
## Trvl#Trvl# Trvl#Trvl# 34 83 35
## Cltr## Cltr## 1 NA 70
## Frgn#Wrld#AsPcfc Frgn#Wrld#AsPcfc 53 150 56
## #Mltmd# #Mltmd# 49 92 52
## TStyl## TStyl## 101 623 105
## #U.S.#Edctn #U.S.#Edctn 82 243 89
## Frgn#Wrld# Frgn#Wrld# 44 128 47
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## OpEd#Opnn# 0.090965862 0.0515046296 0.087700535
## #Opnn#ThPblcEdtr 0.003330558 0.0023148148 0.005347594
## Styls#U.S.# 0.026436303 0.0289351852 0.032620321
## Bsnss#Crsswrds/Gms# 0.021856786 0.0104166667 0.022459893
## Scnc#Hlth# 0.030807660 0.0277777778 0.030481283
## Bsnss#Tchnlgy# 0.044338052 0.0729166667 0.060962567
## ## 0.190049958 0.2146990741 0.182887701
## Bsnss#BsnssDy#Dlbk 0.130932556 0.1869212963 0.162566845
## Mtr#N.Y./Rgn# 0.026644463 0.0405092593 0.035828877
## Cltr#Arts# 0.101998335 0.1070601852 0.093048128
## #Opnn#RmFrDbt 0.008742714 0.0115740741 0.010695187
## Styls##Fshn 0.021648626 0.0086805556 0.008021390
## Bsnss#BsnssDy#SmllBsnss 0.020815987 0.0231481481 0.021925134
## myOthr 0.006869276 0.0028935185 0.002673797
## Trvl#Trvl# 0.017277269 0.0196759259 0.018716578
## Cltr## NA 0.0005787037 0.037433155
## Frgn#Wrld#AsPcfc 0.031223980 0.0306712963 0.029946524
## #Mltmd# 0.019150708 0.0283564815 0.027807487
## TStyl## 0.129683597 0.0584490741 0.056149733
## #U.S.#Edctn 0.050582848 0.0474537037 0.047593583
## Frgn#Wrld# 0.026644463 0.0254629630 0.025133690
## err.abs.fit.sum err.abs.fit.mean .n.fit
## OpEd#Opnn# 169.920774 0.38883472 437
## #Opnn#ThPblcEdtr 7.263079 0.45394245 16
## Styls#U.S.# 62.317403 0.49068821 127
## Bsnss#Crsswrds/Gms# 37.637501 0.35845239 105
## Scnc#Hlth# 67.219902 0.45418853 148
## Bsnss#Tchnlgy# 46.871671 0.22005479 213
## ## 132.756111 0.14540647 913
## Bsnss#BsnssDy#Dlbk 96.765300 0.15383991 629
## Mtr#N.Y./Rgn# 19.934891 0.15574134 128
## Cltr#Arts# 60.231977 0.12292240 490
## #Opnn#RmFrDbt 6.538023 0.15566721 42
## Styls##Fshn 8.953809 0.08609432 104
## Bsnss#BsnssDy#SmllBsnss 13.032416 0.13032416 100
## myOthr 3.723231 0.11282519 33
## Trvl#Trvl# 6.800950 0.08193916 83
## Cltr## NA NA NA
## Frgn#Wrld#AsPcfc 15.573858 0.10382572 150
## #Mltmd# 8.371241 0.09099175 92
## TStyl## 43.550981 0.06990527 623
## #U.S.#Edctn 15.497987 0.06377772 243
## Frgn#Wrld# 8.953551 0.06994962 128
## err.abs.OOB.sum err.abs.OOB.mean
## OpEd#Opnn# 46.5737830 0.52330093
## #Opnn#ThPblcEdtr 2.0080675 0.50201689
## Styls#U.S.# 23.6328413 0.47265683
## Bsnss#Crsswrds/Gms# 8.3911301 0.46617390
## Scnc#Hlth# 22.1189167 0.46081076
## Bsnss#Tchnlgy# 29.7319043 0.23596749
## ## 78.0147094 0.21028224
## Bsnss#BsnssDy#Dlbk 66.1713471 0.20486485
## Mtr#N.Y./Rgn# 13.4746063 0.19249438
## Cltr#Arts# 34.6944525 0.18753758
## #Opnn#RmFrDbt 3.7425266 0.18712633
## Styls##Fshn 2.1565235 0.14376823
## Bsnss#BsnssDy#SmllBsnss 5.6217083 0.14054271
## myOthr 0.5675988 0.11351975
## Trvl#Trvl# 3.6251966 0.10662343
## Cltr## 0.1032201 0.10322006
## Frgn#Wrld#AsPcfc 5.3470704 0.10088812
## #Mltmd# 4.8359493 0.09869284
## TStyl## 9.5035315 0.09409437
## #U.S.#Edctn 6.0212009 0.07342928
## Frgn#Wrld# 3.1996976 0.07272040
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 1728.000000 NA 1870.000000 NA
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 NA NA
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## NA 369.535982 4.690731write.csv(glbObsOOB[, c(glb_id_var,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 276.531 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 12 fit.models 6 2 2 261.729 276.542 14.813
## 13 fit.models 6 3 3 276.543 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glbObsAll, #glbObsTrn, glbObsFit, glbObsOOB, glbObsNew,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 13 fit.models 6 3 3 276.543 282.563
## 14 fit.data.training 7 0 0 282.564 NA
## elapsed
## 13 6.02
## 14 NAStep 7.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
#stop(here"); glb2Sav()
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {
# if (grepl("RFE", glb_sel_mdl_id) ||
# (!is.null(glb_mdl_ensemble) && grepl("RFE", glb_mdl_ensemble))) {
indep_vars <- myadjust_interaction_feats(subset(glb_feats_df,
!nzv & (exclude.as.feat != 1))[, "id"])
rfe_trn_results <-
myrun_rfe(glbObsTrn, indep_vars, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
# }
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjust_interaction_feats(myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glb_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indep_vars = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
indep_vars_vctr <- myextract_actual_feats(predictors(rfe_trn_results))
} else indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
"Final.Ensemble", "Final")
trnobs_df <- if (is.null(glbObsTrnOutliers[[mdl_id_pfx]])) glbObsTrn else
glbObsTrn[!(glbObsTrn[, glb_id_var] %in%
glbObsTrnOutliers[[mdl_id_pfx]]), ]
# Force fitting of Final.glm to identify outliers
#method_vctr <- unique(c("glm", myparseMdlId(glb_sel_mdl_id)$alg))
# or skip glm for speed
method_vctr <- myparseMdlId(glb_sel_mdl_id)$alg
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indep_vars_vctr) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = if (method %in% c("glm", "glmnet")) FALSE else TRUE,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indep_vars = indep_vars_vctr, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
}
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}## +(rfe) fit Fold1.Rep1 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 55
## +(rfe) imp Fold1.Rep1
## -(rfe) imp Fold1.Rep1
## +(rfe) fit Fold1.Rep1 size: 32
## -(rfe) fit Fold1.Rep1 size: 32
## +(rfe) fit Fold1.Rep1 size: 16
## -(rfe) fit Fold1.Rep1 size: 16
## +(rfe) fit Fold1.Rep1 size: 8
## -(rfe) fit Fold1.Rep1 size: 8
## +(rfe) fit Fold1.Rep1 size: 4
## -(rfe) fit Fold1.Rep1 size: 4
## +(rfe) fit Fold1.Rep1 size: 2
## -(rfe) fit Fold1.Rep1 size: 2
## +(rfe) fit Fold2.Rep1 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 55
## +(rfe) imp Fold2.Rep1
## -(rfe) imp Fold2.Rep1
## +(rfe) fit Fold2.Rep1 size: 32
## -(rfe) fit Fold2.Rep1 size: 32
## +(rfe) fit Fold2.Rep1 size: 16
## -(rfe) fit Fold2.Rep1 size: 16
## +(rfe) fit Fold2.Rep1 size: 8
## -(rfe) fit Fold2.Rep1 size: 8
## +(rfe) fit Fold2.Rep1 size: 4
## -(rfe) fit Fold2.Rep1 size: 4
## +(rfe) fit Fold2.Rep1 size: 2
## -(rfe) fit Fold2.Rep1 size: 2
## +(rfe) fit Fold3.Rep1 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 55
## +(rfe) imp Fold3.Rep1
## -(rfe) imp Fold3.Rep1
## +(rfe) fit Fold3.Rep1 size: 32
## -(rfe) fit Fold3.Rep1 size: 32
## +(rfe) fit Fold3.Rep1 size: 16
## -(rfe) fit Fold3.Rep1 size: 16
## +(rfe) fit Fold3.Rep1 size: 8
## -(rfe) fit Fold3.Rep1 size: 8
## +(rfe) fit Fold3.Rep1 size: 4
## -(rfe) fit Fold3.Rep1 size: 4
## +(rfe) fit Fold3.Rep1 size: 2
## -(rfe) fit Fold3.Rep1 size: 2
## +(rfe) fit Fold1.Rep2 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 55
## +(rfe) imp Fold1.Rep2
## -(rfe) imp Fold1.Rep2
## +(rfe) fit Fold1.Rep2 size: 32
## -(rfe) fit Fold1.Rep2 size: 32
## +(rfe) fit Fold1.Rep2 size: 16
## -(rfe) fit Fold1.Rep2 size: 16
## +(rfe) fit Fold1.Rep2 size: 8
## -(rfe) fit Fold1.Rep2 size: 8
## +(rfe) fit Fold1.Rep2 size: 4
## -(rfe) fit Fold1.Rep2 size: 4
## +(rfe) fit Fold1.Rep2 size: 2
## -(rfe) fit Fold1.Rep2 size: 2
## +(rfe) fit Fold2.Rep2 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 55
## +(rfe) imp Fold2.Rep2
## -(rfe) imp Fold2.Rep2
## +(rfe) fit Fold2.Rep2 size: 32
## -(rfe) fit Fold2.Rep2 size: 32
## +(rfe) fit Fold2.Rep2 size: 16
## -(rfe) fit Fold2.Rep2 size: 16
## +(rfe) fit Fold2.Rep2 size: 8
## -(rfe) fit Fold2.Rep2 size: 8
## +(rfe) fit Fold2.Rep2 size: 4
## -(rfe) fit Fold2.Rep2 size: 4
## +(rfe) fit Fold2.Rep2 size: 2
## -(rfe) fit Fold2.Rep2 size: 2
## +(rfe) fit Fold3.Rep2 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 55
## +(rfe) imp Fold3.Rep2
## -(rfe) imp Fold3.Rep2
## +(rfe) fit Fold3.Rep2 size: 32
## -(rfe) fit Fold3.Rep2 size: 32
## +(rfe) fit Fold3.Rep2 size: 16
## -(rfe) fit Fold3.Rep2 size: 16
## +(rfe) fit Fold3.Rep2 size: 8
## -(rfe) fit Fold3.Rep2 size: 8
## +(rfe) fit Fold3.Rep2 size: 4
## -(rfe) fit Fold3.Rep2 size: 4
## +(rfe) fit Fold3.Rep2 size: 2
## -(rfe) fit Fold3.Rep2 size: 2
## +(rfe) fit Fold1.Rep3 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 55
## +(rfe) imp Fold1.Rep3
## -(rfe) imp Fold1.Rep3
## +(rfe) fit Fold1.Rep3 size: 32
## -(rfe) fit Fold1.Rep3 size: 32
## +(rfe) fit Fold1.Rep3 size: 16
## -(rfe) fit Fold1.Rep3 size: 16
## +(rfe) fit Fold1.Rep3 size: 8
## -(rfe) fit Fold1.Rep3 size: 8
## +(rfe) fit Fold1.Rep3 size: 4
## -(rfe) fit Fold1.Rep3 size: 4
## +(rfe) fit Fold1.Rep3 size: 2
## -(rfe) fit Fold1.Rep3 size: 2
## +(rfe) fit Fold2.Rep3 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 55
## +(rfe) imp Fold2.Rep3
## -(rfe) imp Fold2.Rep3
## +(rfe) fit Fold2.Rep3 size: 32
## -(rfe) fit Fold2.Rep3 size: 32
## +(rfe) fit Fold2.Rep3 size: 16
## -(rfe) fit Fold2.Rep3 size: 16
## +(rfe) fit Fold2.Rep3 size: 8
## -(rfe) fit Fold2.Rep3 size: 8
## +(rfe) fit Fold2.Rep3 size: 4
## -(rfe) fit Fold2.Rep3 size: 4
## +(rfe) fit Fold2.Rep3 size: 2
## -(rfe) fit Fold2.Rep3 size: 2
## +(rfe) fit Fold3.Rep3 size: 55## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 55
## +(rfe) imp Fold3.Rep3
## -(rfe) imp Fold3.Rep3
## +(rfe) fit Fold3.Rep3 size: 32
## -(rfe) fit Fold3.Rep3 size: 32
## +(rfe) fit Fold3.Rep3 size: 16
## -(rfe) fit Fold3.Rep3 size: 16
## +(rfe) fit Fold3.Rep3 size: 8
## -(rfe) fit Fold3.Rep3 size: 8
## +(rfe) fit Fold3.Rep3 size: 4
## -(rfe) fit Fold3.Rep3 size: 4
## +(rfe) fit Fold3.Rep3 size: 2
## -(rfe) fit Fold3.Rep3 size: 2## Warning in lda.default(x, grouping, ...): variables are collinear##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 2 0.8204 0.03718 0.003445 0.01035
## 4 0.8760 0.44502 0.003122 0.01919
## 8 0.8736 0.44378 0.003353 0.02080
## 16 0.9015 0.63767 0.006518 0.02345
## 32 0.9010 0.63663 0.006363 0.02247
## 55 0.9029 0.64631 0.006957 0.02433 *
##
## The top 5 variables (out of 55):
## WordCount.log1p, WordCount.root2, WordCount.nexp, NDSSName.my.fctrOpEd#Opnn#, PubDate.day.minutes.poly.1
##
## [1] "WordCount.log1p"
## [2] "WordCount.root2"
## [3] "WordCount.nexp"
## [4] "NDSSName.my.fctrOpEd#Opnn#"
## [5] "PubDate.day.minutes.poly.1"
## [6] "PubDate.day.minutes.poly.4"
## [7] "PubDate.hour.fctr(15.3,23]"
## [8] "PubDate.last2.log1p"
## [9] "PubDate.last4.log1p"
## [10] "NDSSName.my.fctrScnc#Hlth#"
## [11] "NDSSName.my.fctrBsnss#Crsswrds/Gms#"
## [12] "PubDate.day.minutes.poly.5"
## [13] "PubDate.last8.log1p"
## [14] "NDSSName.my.fctrStyls#U.S.#"
## [15] "PubDate.wkend"
## [16] "PubDate.last16.log1p"
## [17] "PubDate.day.minutes.poly.2"
## [18] "PubDate.juliandate"
## [19] "PubDate.wkday.fctr6"
## [20] "PubDate.month.fctr11"
## [21] "PubDate.second.fctr(14.8,29.5]"
## [22] "PubDate.date.fctr(7,13]"
## [23] "PubDate.last32.log1p"
## [24] ".rnorm"
## [25] "PubDate.wkday.fctr1"
## [26] "PubDate.day.minutes.poly.3"
## [27] "PubDate.date.fctr(25,31]"
## [28] "PubDate.hour.fctr(7.67,15.3]"
## [29] "PubDate.minute.fctr(14.8,29.5]"
## [30] "PubDate.month.fctr10"
## [31] "NDSSName.my.fctrBsnss#Tchnlgy#"
## [32] "PubDate.second.fctr(29.5,44.2]"
## [33] "NDSSName.my.fctrmyOthr"
## [34] "PubDate.date.fctr(13,19]"
## [35] "PubDate.wkday.fctr3"
## [36] "PubDate.minute.fctr(44.2,59.1]"
## [37] "PubDate.wkday.fctr4"
## [38] "PubDate.second.fctr(44.2,59.1]"
## [39] "NDSSName.my.fctr#Opnn#RmFrDbt"
## [40] "PubDate.date.fctr(19,25]"
## [41] "NDSSName.my.fctrMtr#N.Y./Rgn#"
## [42] "NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss"
## [43] "NDSSName.my.fctrTrvl#Trvl#"
## [44] "NDSSName.my.fctrStyls##Fshn"
## [45] "NDSSName.my.fctr#Mltmd#"
## [46] "PubDate.wkday.fctr2"
## [47] "NDSSName.my.fctrFrgn#Wrld#"
## [48] "NDSSName.my.fctrFrgn#Wrld#AsPcfc"
## [49] "PubDate.wkday.fctr5"
## [50] "PubDate.minute.fctr(29.5,44.2]"
## [51] "NDSSName.my.fctr#U.S.#Edctn"
## [52] "NDSSName.my.fctrCltr#Arts#"
## [53] "NDSSName.my.fctrBsnss#BsnssDy#Dlbk"
## [54] "NDSSName.my.fctr##"
## [55] "NDSSName.my.fctrTStyl##"
## [1] "fitting model: Final##rcv#glmnet"
## [1] " indep_vars: WordCount.root2,WordCount.log1p,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.hour.fctr,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.last2.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,PubDate.juliandate,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5"
## + Fold1.Rep1: alpha=0.100, lambda=0.07781
## - Fold1.Rep1: alpha=0.100, lambda=0.07781
## + Fold1.Rep1: alpha=0.325, lambda=0.07781
## - Fold1.Rep1: alpha=0.325, lambda=0.07781
## + Fold1.Rep1: alpha=0.550, lambda=0.07781
## - Fold1.Rep1: alpha=0.550, lambda=0.07781
## + Fold1.Rep1: alpha=0.775, lambda=0.07781
## - Fold1.Rep1: alpha=0.775, lambda=0.07781
## + Fold1.Rep1: alpha=1.000, lambda=0.07781
## - Fold1.Rep1: alpha=1.000, lambda=0.07781
## + Fold2.Rep1: alpha=0.100, lambda=0.07781
## - Fold2.Rep1: alpha=0.100, lambda=0.07781
## + Fold2.Rep1: alpha=0.325, lambda=0.07781
## - Fold2.Rep1: alpha=0.325, lambda=0.07781
## + Fold2.Rep1: alpha=0.550, lambda=0.07781
## - Fold2.Rep1: alpha=0.550, lambda=0.07781
## + Fold2.Rep1: alpha=0.775, lambda=0.07781
## - Fold2.Rep1: alpha=0.775, lambda=0.07781
## + Fold2.Rep1: alpha=1.000, lambda=0.07781
## - Fold2.Rep1: alpha=1.000, lambda=0.07781
## + Fold3.Rep1: alpha=0.100, lambda=0.07781
## - Fold3.Rep1: alpha=0.100, lambda=0.07781
## + Fold3.Rep1: alpha=0.325, lambda=0.07781
## - Fold3.Rep1: alpha=0.325, lambda=0.07781
## + Fold3.Rep1: alpha=0.550, lambda=0.07781
## - Fold3.Rep1: alpha=0.550, lambda=0.07781
## + Fold3.Rep1: alpha=0.775, lambda=0.07781
## - Fold3.Rep1: alpha=0.775, lambda=0.07781
## + Fold3.Rep1: alpha=1.000, lambda=0.07781
## - Fold3.Rep1: alpha=1.000, lambda=0.07781
## + Fold1.Rep2: alpha=0.100, lambda=0.07781
## - Fold1.Rep2: alpha=0.100, lambda=0.07781
## + Fold1.Rep2: alpha=0.325, lambda=0.07781
## - Fold1.Rep2: alpha=0.325, lambda=0.07781
## + Fold1.Rep2: alpha=0.550, lambda=0.07781
## - Fold1.Rep2: alpha=0.550, lambda=0.07781
## + Fold1.Rep2: alpha=0.775, lambda=0.07781
## - Fold1.Rep2: alpha=0.775, lambda=0.07781
## + Fold1.Rep2: alpha=1.000, lambda=0.07781
## - Fold1.Rep2: alpha=1.000, lambda=0.07781
## + Fold2.Rep2: alpha=0.100, lambda=0.07781
## - Fold2.Rep2: alpha=0.100, lambda=0.07781
## + Fold2.Rep2: alpha=0.325, lambda=0.07781
## - Fold2.Rep2: alpha=0.325, lambda=0.07781
## + Fold2.Rep2: alpha=0.550, lambda=0.07781
## - Fold2.Rep2: alpha=0.550, lambda=0.07781
## + Fold2.Rep2: alpha=0.775, lambda=0.07781
## - Fold2.Rep2: alpha=0.775, lambda=0.07781
## + Fold2.Rep2: alpha=1.000, lambda=0.07781
## - Fold2.Rep2: alpha=1.000, lambda=0.07781
## + Fold3.Rep2: alpha=0.100, lambda=0.07781
## - Fold3.Rep2: alpha=0.100, lambda=0.07781
## + Fold3.Rep2: alpha=0.325, lambda=0.07781
## - Fold3.Rep2: alpha=0.325, lambda=0.07781
## + Fold3.Rep2: alpha=0.550, lambda=0.07781
## - Fold3.Rep2: alpha=0.550, lambda=0.07781
## + Fold3.Rep2: alpha=0.775, lambda=0.07781
## - Fold3.Rep2: alpha=0.775, lambda=0.07781
## + Fold3.Rep2: alpha=1.000, lambda=0.07781
## - Fold3.Rep2: alpha=1.000, lambda=0.07781
## + Fold1.Rep3: alpha=0.100, lambda=0.07781
## - Fold1.Rep3: alpha=0.100, lambda=0.07781
## + Fold1.Rep3: alpha=0.325, lambda=0.07781
## - Fold1.Rep3: alpha=0.325, lambda=0.07781
## + Fold1.Rep3: alpha=0.550, lambda=0.07781
## - Fold1.Rep3: alpha=0.550, lambda=0.07781
## + Fold1.Rep3: alpha=0.775, lambda=0.07781
## - Fold1.Rep3: alpha=0.775, lambda=0.07781
## + Fold1.Rep3: alpha=1.000, lambda=0.07781
## - Fold1.Rep3: alpha=1.000, lambda=0.07781
## + Fold2.Rep3: alpha=0.100, lambda=0.07781
## - Fold2.Rep3: alpha=0.100, lambda=0.07781
## + Fold2.Rep3: alpha=0.325, lambda=0.07781
## - Fold2.Rep3: alpha=0.325, lambda=0.07781
## + Fold2.Rep3: alpha=0.550, lambda=0.07781
## - Fold2.Rep3: alpha=0.550, lambda=0.07781
## + Fold2.Rep3: alpha=0.775, lambda=0.07781
## - Fold2.Rep3: alpha=0.775, lambda=0.07781
## + Fold2.Rep3: alpha=1.000, lambda=0.07781
## - Fold2.Rep3: alpha=1.000, lambda=0.07781
## + Fold3.Rep3: alpha=0.100, lambda=0.07781
## - Fold3.Rep3: alpha=0.100, lambda=0.07781
## + Fold3.Rep3: alpha=0.325, lambda=0.07781
## - Fold3.Rep3: alpha=0.325, lambda=0.07781
## + Fold3.Rep3: alpha=0.550, lambda=0.07781
## - Fold3.Rep3: alpha=0.550, lambda=0.07781
## + Fold3.Rep3: alpha=0.775, lambda=0.07781
## - Fold3.Rep3: alpha=0.775, lambda=0.07781
## + Fold3.Rep3: alpha=1.000, lambda=0.07781
## - Fold3.Rep3: alpha=1.000, lambda=0.07781
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.00361 on full training set
## Length Class Mode
## a0 93 -none- numeric
## beta 5301 dgCMatrix S4
## df 93 -none- numeric
## dim 2 -none- numeric
## lambda 93 -none- numeric
## dev.ratio 93 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 57 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -6.71740932
## NDSSName.my.fctr#Mltmd#
## -0.95451508
## NDSSName.my.fctr#Opnn#RmFrDbt
## -3.04595818
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.15684109
## NDSSName.my.fctr#U.S.#Edctn
## -1.88518827
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -0.24938082
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -0.73887006
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.21815213
## NDSSName.my.fctrBsnss#Tchnlgy#
## 0.44788750
## NDSSName.my.fctrCltr#Arts#
## -0.18319632
## NDSSName.my.fctrFrgn#Wrld#
## -1.20835894
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -1.61909013
## NDSSName.my.fctrOpEd#Opnn#
## 3.71825778
## NDSSName.my.fctrScnc#Hlth#
## 2.69372416
## NDSSName.my.fctrStyls##Fshn
## -1.34904273
## NDSSName.my.fctrStyls#U.S.#
## 2.46572272
## NDSSName.my.fctrTStyl##
## -1.33936830
## NDSSName.my.fctrTrvl#Trvl#
## -0.68785697
## NDSSName.my.fctrmyOthr
## -1.37260505
## PubDate.date.fctr(7,13]
## 0.03756858
## PubDate.date.fctr(13,19]
## -0.04997442
## PubDate.date.fctr(25,31]
## 0.02915688
## PubDate.day.minutes.poly.1
## 14.63604197
## PubDate.day.minutes.poly.2
## 19.28093122
## PubDate.day.minutes.poly.3
## 3.42079095
## PubDate.day.minutes.poly.4
## 2.07653730
## PubDate.hour.fctr(7.67,15.3]
## 0.24042550
## PubDate.last16.log1p
## 0.06699389
## PubDate.last2.log1p
## 0.01463237
## PubDate.minute.fctr(29.5,44.2]
## -0.15908416
## PubDate.month.fctr11
## -0.07224476
## PubDate.second.fctr(44.2,59.1]
## -0.08388152
## PubDate.wkday.fctr1
## 0.09440272
## PubDate.wkday.fctr2
## -0.03634627
## PubDate.wkday.fctr5
## -0.15064565
## PubDate.wkday.fctr6
## -0.21128855
## PubDate.wkend
## 0.42922277
## WordCount.log1p
## 0.38451519
## WordCount.root2
## 0.05236843
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -6.830834e+00
## NDSSName.my.fctr#Mltmd#
## -1.020841e+00
## NDSSName.my.fctr#Opnn#RmFrDbt
## -3.136910e+00
## NDSSName.my.fctr#Opnn#ThPblcEdtr
## 3.164358e+00
## NDSSName.my.fctr#U.S.#Edctn
## -1.985122e+00
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk
## -2.726760e-01
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss
## -7.755608e-01
## NDSSName.my.fctrBsnss#Crsswrds/Gms#
## 3.214818e+00
## NDSSName.my.fctrBsnss#Tchnlgy#
## 4.430681e-01
## NDSSName.my.fctrCltr#Arts#
## -2.116312e-01
## NDSSName.my.fctrFrgn#Wrld#
## -1.304342e+00
## NDSSName.my.fctrFrgn#Wrld#AsPcfc
## -1.682973e+00
## NDSSName.my.fctrOpEd#Opnn#
## 3.721324e+00
## NDSSName.my.fctrScnc#Hlth#
## 2.688810e+00
## NDSSName.my.fctrStyls##Fshn
## -1.425434e+00
## NDSSName.my.fctrStyls#U.S.#
## 2.459984e+00
## NDSSName.my.fctrTStyl##
## -1.387084e+00
## NDSSName.my.fctrTrvl#Trvl#
## -7.480692e-01
## NDSSName.my.fctrmyOthr
## -1.482128e+00
## PubDate.date.fctr(7,13]
## 4.406889e-02
## PubDate.date.fctr(13,19]
## -5.356303e-02
## PubDate.date.fctr(25,31]
## 3.547036e-02
## PubDate.day.minutes.poly.1
## 1.496003e+01
## PubDate.day.minutes.poly.2
## 2.037019e+01
## PubDate.day.minutes.poly.3
## 3.488587e+00
## PubDate.day.minutes.poly.4
## 1.513324e+00
## PubDate.hour.fctr(7.67,15.3]
## 2.728426e-01
## PubDate.juliandate
## -1.676444e-05
## PubDate.last16.log1p
## 7.368211e-02
## PubDate.last2.log1p
## 1.445898e-02
## PubDate.minute.fctr(29.5,44.2]
## -1.653137e-01
## PubDate.month.fctr11
## -7.889861e-02
## PubDate.second.fctr(44.2,59.1]
## -9.011477e-02
## PubDate.wkday.fctr1
## 9.895137e-02
## PubDate.wkday.fctr2
## -4.419254e-02
## PubDate.wkday.fctr5
## -1.577937e-01
## PubDate.wkday.fctr6
## -2.387115e-01
## PubDate.wkend
## 4.346004e-01
## WordCount.log1p
## 3.934762e-01
## WordCount.root2
## 5.234272e-02
## Prediction
## Reference N Y
## N 4967 472
## Y 220 873
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.940600e-01 6.518908e-01 8.863433e-01 9.014229e-01 8.326699e-01
## AccuracyPValue McnemarPValue
## 3.486892e-45 1.406613e-21
## id
## 1 Final##rcv#glmnet
## feats
## 1 WordCount.root2,WordCount.log1p,NDSSName.my.fctr,PubDate.day.minutes.poly.1,PubDate.hour.fctr,PubDate.wkend,PubDate.day.minutes.poly.4,PubDate.day.minutes.poly.2,PubDate.last4.log1p,PubDate.last2.log1p,PubDate.last8.log1p,PubDate.last16.log1p,PubDate.day.minutes.poly.3,PubDate.last32.log1p,PubDate.month.fctr,PubDate.juliandate,.rnorm,PubDate.date.fctr,PubDate.second.fctr,PubDate.minute.fctr,PubDate.wkday.fctr,WordCount.nexp,PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 37.175 0.898
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8012801 0.9612061 0.6413541 0.9361337
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.2 0.7161608 0.9063073
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8863433 0.9014229 0.6394839
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005622392 0.02614856rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 14 fit.data.training 7 0 0 282.564 347.894
## 15 fit.data.training 7 1 1 347.894 NA
## elapsed
## 14 65.33
## 15 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glb_fin_mdl_id)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id, :
## Using default probability threshold: 0.1glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X##rcv#glmnet.imp imp
## PubDate.day.minutes.poly.2 22.879214 100.000000
## PubDate.day.minutes.poly.1 100.000000 78.090459
## NDSSName.my.fctrOpEd#Opnn# 29.608813 29.735703
## PubDate.day.minutes.poly.3 6.181073 28.574510
## NDSSName.my.fctrBsnss#Crsswrds/Gms# 27.135657 27.538396
## NDSSName.my.fctr#Opnn#ThPblcEdtr 24.849247 27.293763
## NDSSName.my.fctrScnc#Hlth# 24.964890 25.245138
## NDSSName.my.fctrStyls#U.S.# 23.449567 24.247822
## PubDate.day.minutes.poly.4 46.118345 21.362667
## NDSSName.my.fctrBsnss#Tchnlgy# 6.181073 15.438969
## PubDate.wkend 7.555716 15.379160
## WordCount.log1p 7.599061 15.191568
## PubDate.hour.fctr(7.67,15.3] 6.181073 14.612298
## PubDate.wkday.fctr1 6.181073 13.915416
## PubDate.last16.log1p 6.181073 13.800288
## WordCount.root2 6.405483 13.722145
## PubDate.date.fctr(7,13] 6.181073 13.671403
## PubDate.date.fctr(25,31] 6.181073 13.634277
## PubDate.last2.log1p 6.307833 13.557058
## .rnorm 6.181073 13.493535
## NDSSName.my.fctrCltr## 6.181073 13.493535
## NDSSName.my.fctrMtr#N.Y./Rgn# 6.181073 13.493535
## PubDate.date.fctr(19,25] 6.181073 13.493535
## PubDate.day.minutes.poly.5 6.181073 13.493535
## PubDate.hour.fctr(15.3,23] 6.568901 13.493535
## PubDate.last32.log1p 6.181073 13.493535
## PubDate.last4.log1p 6.403279 13.493535
## PubDate.last8.log1p 6.231915 13.493535
## PubDate.minute.fctr(14.8,29.5] 6.181073 13.493535
## PubDate.minute.fctr(44.2,59.1] 6.181073 13.493535
## PubDate.month.fctr10 6.181073 13.493535
## PubDate.month.fctr12 6.181073 13.493535
## PubDate.second.fctr(14.8,29.5] 6.181073 13.493535
## PubDate.second.fctr(29.5,44.2] 6.181073 13.493535
## PubDate.wkday.fctr3 6.181073 13.493535
## PubDate.wkday.fctr4 6.181073 13.493535
## WordCount.nexp 6.181073 13.493535
## PubDate.juliandate 6.181073 13.493499
## PubDate.wkday.fctr2 6.181073 13.318140
## PubDate.date.fctr(13,19] 6.181073 13.267690
## PubDate.month.fctr11 6.181073 13.163927
## PubDate.second.fctr(44.2,59.1] 6.181073 13.114010
## PubDate.wkday.fctr5 6.181073 12.820541
## PubDate.minute.fctr(29.5,44.2] 6.181073 12.785649
## NDSSName.my.fctrCltr#Arts# 4.494666 12.633131
## PubDate.wkday.fctr6 6.181073 12.512620
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk 4.575817 12.355071
## NDSSName.my.fctrTrvl#Trvl# 4.901316 10.361955
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss 4.791503 10.189240
## NDSSName.my.fctr#Mltmd# 5.672845 9.184598
## NDSSName.my.fctrFrgn#Wrld# 4.664559 8.013114
## NDSSName.my.fctrTStyl## 2.131448 7.543734
## NDSSName.my.fctrStyls##Fshn 3.582132 7.440496
## NDSSName.my.fctrmyOthr 6.019416 7.267138
## NDSSName.my.fctrFrgn#Wrld#AsPcfc 3.104915 6.287949
## NDSSName.my.fctr#U.S.#Edctn 3.501802 5.049357
## NDSSName.my.fctr#Opnn#RmFrDbt 0.000000 0.000000
## Final##rcv#glmnet.imp
## PubDate.day.minutes.poly.2 100.000000
## PubDate.day.minutes.poly.1 78.090459
## NDSSName.my.fctrOpEd#Opnn# 29.735703
## PubDate.day.minutes.poly.3 28.574510
## NDSSName.my.fctrBsnss#Crsswrds/Gms# 27.538396
## NDSSName.my.fctr#Opnn#ThPblcEdtr 27.293763
## NDSSName.my.fctrScnc#Hlth# 25.245138
## NDSSName.my.fctrStyls#U.S.# 24.247822
## PubDate.day.minutes.poly.4 21.362667
## NDSSName.my.fctrBsnss#Tchnlgy# 15.438969
## PubDate.wkend 15.379160
## WordCount.log1p 15.191568
## PubDate.hour.fctr(7.67,15.3] 14.612298
## PubDate.wkday.fctr1 13.915416
## PubDate.last16.log1p 13.800288
## WordCount.root2 13.722145
## PubDate.date.fctr(7,13] 13.671403
## PubDate.date.fctr(25,31] 13.634277
## PubDate.last2.log1p 13.557058
## .rnorm 13.493535
## NDSSName.my.fctrCltr## 13.493535
## NDSSName.my.fctrMtr#N.Y./Rgn# 13.493535
## PubDate.date.fctr(19,25] 13.493535
## PubDate.day.minutes.poly.5 13.493535
## PubDate.hour.fctr(15.3,23] 13.493535
## PubDate.last32.log1p 13.493535
## PubDate.last4.log1p 13.493535
## PubDate.last8.log1p 13.493535
## PubDate.minute.fctr(14.8,29.5] 13.493535
## PubDate.minute.fctr(44.2,59.1] 13.493535
## PubDate.month.fctr10 13.493535
## PubDate.month.fctr12 13.493535
## PubDate.second.fctr(14.8,29.5] 13.493535
## PubDate.second.fctr(29.5,44.2] 13.493535
## PubDate.wkday.fctr3 13.493535
## PubDate.wkday.fctr4 13.493535
## WordCount.nexp 13.493535
## PubDate.juliandate 13.493499
## PubDate.wkday.fctr2 13.318140
## PubDate.date.fctr(13,19] 13.267690
## PubDate.month.fctr11 13.163927
## PubDate.second.fctr(44.2,59.1] 13.114010
## PubDate.wkday.fctr5 12.820541
## PubDate.minute.fctr(29.5,44.2] 12.785649
## NDSSName.my.fctrCltr#Arts# 12.633131
## PubDate.wkday.fctr6 12.512620
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk 12.355071
## NDSSName.my.fctrTrvl#Trvl# 10.361955
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss 10.189240
## NDSSName.my.fctr#Mltmd# 9.184598
## NDSSName.my.fctrFrgn#Wrld# 8.013114
## NDSSName.my.fctrTStyl## 7.543734
## NDSSName.my.fctrStyls##Fshn 7.440496
## NDSSName.my.fctrmyOthr 7.267138
## NDSSName.my.fctrFrgn#Wrld#AsPcfc 6.287949
## NDSSName.my.fctr#U.S.#Edctn 5.049357
## NDSSName.my.fctr#Opnn#RmFrDbt 0.000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 23
## [1] "Min/Max Boundaries: "
## UniqueID Pplr.fctr Pplr.fctr.All.X..rcv.glmnet.prob
## 1 1065 N NA
## 2 4168 N 0.0411419
## 3 5647 N 0.1290645
## 4 302 N NA
## Pplr.fctr.All.X..rcv.glmnet Pplr.fctr.All.X..rcv.glmnet.err
## 1 <NA> NA
## 2 N FALSE
## 3 Y TRUE
## 4 <NA> NA
## Pplr.fctr.All.X..rcv.glmnet.err.abs Pplr.fctr.All.X..rcv.glmnet.is.acc
## 1 NA NA
## 2 0.0411419 TRUE
## 3 0.1290645 FALSE
## 4 NA NA
## Pplr.fctr.Final..rcv.glmnet.prob Pplr.fctr.Final..rcv.glmnet
## 1 0.022283474 N
## 2 0.007666321 N
## 3 0.105843562 Y
## 4 0.436516680 Y
## Pplr.fctr.Final..rcv.glmnet.err Pplr.fctr.Final..rcv.glmnet.err.abs
## 1 FALSE 0.022283474
## 2 FALSE 0.007666321
## 3 TRUE 0.105843562
## 4 TRUE 0.436516680
## Pplr.fctr.Final..rcv.glmnet.is.acc Pplr.fctr.Final..rcv.glmnet.accurate
## 1 TRUE TRUE
## 2 TRUE TRUE
## 3 FALSE FALSE
## 4 FALSE FALSE
## Pplr.fctr.Final..rcv.glmnet.error .label
## 1 0.000000000 1065
## 2 0.000000000 4168
## 3 0.005843562 5647
## 4 0.336516680 302
## [1] "Inaccurate: "
## UniqueID Pplr.fctr Pplr.fctr.All.X..rcv.glmnet.prob
## 1 2182 Y 0.04423435
## 2 4352 Y NA
## 3 5486 Y NA
## 4 4721 Y 0.06779771
## 5 1696 Y 0.05294423
## 6 364 Y 0.06987153
## Pplr.fctr.All.X..rcv.glmnet Pplr.fctr.All.X..rcv.glmnet.err
## 1 N TRUE
## 2 <NA> NA
## 3 <NA> NA
## 4 N TRUE
## 5 N TRUE
## 6 N TRUE
## Pplr.fctr.All.X..rcv.glmnet.err.abs Pplr.fctr.All.X..rcv.glmnet.is.acc
## 1 0.9557656 FALSE
## 2 NA NA
## 3 NA NA
## 4 0.9322023 FALSE
## 5 0.9470558 FALSE
## 6 0.9301285 FALSE
## Pplr.fctr.Final..rcv.glmnet.prob Pplr.fctr.Final..rcv.glmnet
## 1 0.006646728 N
## 2 0.009472057 N
## 3 0.013199051 N
## 4 0.013513861 N
## 5 0.015668226 N
## 6 0.019168273 N
## Pplr.fctr.Final..rcv.glmnet.err Pplr.fctr.Final..rcv.glmnet.err.abs
## 1 TRUE 0.9933533
## 2 TRUE 0.9905279
## 3 TRUE 0.9868009
## 4 TRUE 0.9864861
## 5 TRUE 0.9843318
## 6 TRUE 0.9808317
## Pplr.fctr.Final..rcv.glmnet.is.acc Pplr.fctr.Final..rcv.glmnet.accurate
## 1 FALSE FALSE
## 2 FALSE FALSE
## 3 FALSE FALSE
## 4 FALSE FALSE
## 5 FALSE FALSE
## 6 FALSE FALSE
## Pplr.fctr.Final..rcv.glmnet.error
## 1 -0.09335327
## 2 -0.09052794
## 3 -0.08680095
## 4 -0.08648614
## 5 -0.08433177
## 6 -0.08083173
## UniqueID Pplr.fctr Pplr.fctr.All.X..rcv.glmnet.prob
## 190 789 N 0.1059522
## 220 3588 N NA
## 380 3380 N NA
## 410 72 N 0.1364144
## 905 2226 N 0.1913497
## 1105 5462 N NA
## Pplr.fctr.All.X..rcv.glmnet Pplr.fctr.All.X..rcv.glmnet.err
## 190 Y TRUE
## 220 <NA> NA
## 380 <NA> NA
## 410 Y TRUE
## 905 Y TRUE
## 1105 <NA> NA
## Pplr.fctr.All.X..rcv.glmnet.err.abs
## 190 0.1059522
## 220 NA
## 380 NA
## 410 0.1364144
## 905 0.1913497
## 1105 NA
## Pplr.fctr.All.X..rcv.glmnet.is.acc Pplr.fctr.Final..rcv.glmnet.prob
## 190 FALSE 0.1129611
## 220 NA 0.1179667
## 380 NA 0.1378456
## 410 FALSE 0.1413828
## 905 FALSE 0.3794899
## 1105 NA 0.7300482
## Pplr.fctr.Final..rcv.glmnet Pplr.fctr.Final..rcv.glmnet.err
## 190 Y TRUE
## 220 Y TRUE
## 380 Y TRUE
## 410 Y TRUE
## 905 Y TRUE
## 1105 Y TRUE
## Pplr.fctr.Final..rcv.glmnet.err.abs
## 190 0.1129611
## 220 0.1179667
## 380 0.1378456
## 410 0.1413828
## 905 0.3794899
## 1105 0.7300482
## Pplr.fctr.Final..rcv.glmnet.is.acc
## 190 FALSE
## 220 FALSE
## 380 FALSE
## 410 FALSE
## 905 FALSE
## 1105 FALSE
## Pplr.fctr.Final..rcv.glmnet.accurate
## 190 FALSE
## 220 FALSE
## 380 FALSE
## 410 FALSE
## 905 FALSE
## 1105 FALSE
## Pplr.fctr.Final..rcv.glmnet.error
## 190 0.01296111
## 220 0.01796670
## 380 0.03784555
## 410 0.04138278
## 905 0.27948986
## 1105 0.63004823
## UniqueID Pplr.fctr Pplr.fctr.All.X..rcv.glmnet.prob
## 1182 770 N NA
## 1183 2179 N 0.7720249
## 1184 472 N NA
## 1185 2995 N NA
## 1186 1612 N 0.7937303
## 1187 1448 N NA
## Pplr.fctr.All.X..rcv.glmnet Pplr.fctr.All.X..rcv.glmnet.err
## 1182 <NA> NA
## 1183 Y TRUE
## 1184 <NA> NA
## 1185 <NA> NA
## 1186 Y TRUE
## 1187 <NA> NA
## Pplr.fctr.All.X..rcv.glmnet.err.abs
## 1182 NA
## 1183 0.7720249
## 1184 NA
## 1185 NA
## 1186 0.7937303
## 1187 NA
## Pplr.fctr.All.X..rcv.glmnet.is.acc Pplr.fctr.Final..rcv.glmnet.prob
## 1182 NA 0.9518142
## 1183 FALSE 0.9542775
## 1184 NA 0.9580719
## 1185 NA 0.9628305
## 1186 FALSE 0.9643315
## 1187 NA 0.9689304
## Pplr.fctr.Final..rcv.glmnet Pplr.fctr.Final..rcv.glmnet.err
## 1182 Y TRUE
## 1183 Y TRUE
## 1184 Y TRUE
## 1185 Y TRUE
## 1186 Y TRUE
## 1187 Y TRUE
## Pplr.fctr.Final..rcv.glmnet.err.abs
## 1182 0.9518142
## 1183 0.9542775
## 1184 0.9580719
## 1185 0.9628305
## 1186 0.9643315
## 1187 0.9689304
## Pplr.fctr.Final..rcv.glmnet.is.acc
## 1182 FALSE
## 1183 FALSE
## 1184 FALSE
## 1185 FALSE
## 1186 FALSE
## 1187 FALSE
## Pplr.fctr.Final..rcv.glmnet.accurate
## 1182 FALSE
## 1183 FALSE
## 1184 FALSE
## 1185 FALSE
## 1186 FALSE
## 1187 FALSE
## Pplr.fctr.Final..rcv.glmnet.error
## 1182 0.8518142
## 1183 0.8542775
## 1184 0.8580719
## 1185 0.8628305
## 1186 0.8643315
## 1187 0.8689304
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Pplr.fctr.Final..rcv.glmnet.prob"
## [2] "Pplr.fctr.Final..rcv.glmnet"
## [3] "Pplr.fctr.Final..rcv.glmnet.err"
## [4] "Pplr.fctr.Final..rcv.glmnet.err.abs"
## [5] "Pplr.fctr.Final..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)if (glb_save_envir)
save(glb_feats_df, glbObsAll,
#glbObsTrn, glbObsFit, glbObsOOB, glbObsNew,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 15 fit.data.training 7 1 1 347.894 358.247
## 16 predict.data.new 8 0 0 358.247 NA
## elapsed
## 15 10.353
## 16 NAStep 8.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.1## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.1## Warning in zoo(rval[i], index(x)[i]): some methods for "zoo" objects do not
## work if the index entries in 'order.by' are not unique## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 23## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## NULL## Loading required package: stringr## [1] "ObsNew Prediction errors in categories:"
## NDSSName.my.fctr .n.Trn.N .n.Trn.Y .n.New.N .n.New.Y
## 5 #U.S.#Edctn 325 NA 87 2
## 10 Cltr## 1 NA 47 23
## .n.Trn.N .n.Trn.Y .n.New.N .n.New.Y
## 326 0 134 25## Loading required package: tidyr
##
## Attaching package: 'tidyr'
##
## The following object is masked from 'package:Matrix':
##
## expand
##
## The following object is masked from 'package:mice':
##
## complete## [1] "OOBobs Pplr.fctr.All.X..rcv.glmnet Y: min < min of Train range: 12"
## UniqueID Pplr.fctr.All.X..rcv.glmnet PubDate.day.minutes.poly.2
## 1751 1751 Y -0.001148534
## 639 639 Y 0.009120600
## 1542 1542 Y -0.008640045
## 1535 1535 Y -0.008325008
## 2645 2645 Y 0.012594250
## 697 697 Y -0.008337839
## 2382 2382 Y -0.006921772
## 5630 5630 Y -0.007831741
## 1677 1677 Y -0.008708358
## 6435 6435 Y 0.002105741
## 1871 1871 Y -0.001039862
## 4223 4223 Y -0.008758791
## PubDate.day.minutes.poly.4 PubDate.last2.log1p PubDate.last32.log1p
## 1751 -0.007894421 5.463832 9.151227
## 639 -0.017000978 2.995732 10.597135
## 1542 0.008801265 6.313548 9.217117
## 1535 0.007631110 5.111988 9.222862
## 2645 -0.018240126 8.943637 10.695801
## 697 0.007674714 6.338594 9.641213
## 2382 0.003523823 6.318968 9.397235
## 5630 0.006070276 6.104793 9.495294
## 1677 0.009654246 4.890349 9.758866
## 6435 -0.010140008 7.624131 10.866967
## 1871 -0.008047440 4.804021 9.176990
## 4223 0.009518864 8.350902 11.242625
## PubDate.last8.log1p WordCount.log1p WordCount.root2
## 1751 7.571988 7.070724 34.292856
## 639 10.093116 6.986566 32.878564
## 1542 7.846981 7.135687 35.425979
## 1535 7.569928 6.463029 25.298221
## 2645 10.083765 6.635947 27.586228
## 697 7.019297 6.415097 24.698178
## 2382 7.138867 5.755742 17.748239
## 5630 7.152269 3.737670 6.403124
## 1677 7.124478 6.756932 29.308702
## 6435 8.695172 0.000000 0.000000
## 1871 7.187657 7.034388 33.674916
## 4223 10.840972 7.274480 37.973675
## id cor.y
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.2 0.07097772
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 0.07394139
## PubDate.last2.log1p PubDate.last2.log1p 0.06567954
## PubDate.last32.log1p PubDate.last32.log1p 0.02254251
## PubDate.last8.log1p PubDate.last8.log1p 0.05657458
## WordCount.log1p WordCount.log1p 0.25431963
## WordCount.root2 WordCount.root2 0.29212068
## exclude.as.feat cor.y.abs cor.high.X
## PubDate.day.minutes.poly.2 FALSE 0.07097772 <NA>
## PubDate.day.minutes.poly.4 FALSE 0.07394139 <NA>
## PubDate.last2.log1p FALSE 0.06567954 PubDate.last4.log1p
## PubDate.last32.log1p FALSE 0.02254251 <NA>
## PubDate.last8.log1p FALSE 0.05657458 PubDate.last4.log1p
## WordCount.log1p FALSE 0.25431963 WordCount.root2
## WordCount.root2 FALSE 0.29212068 <NA>
## freqRatio percentUnique zeroVar nzv
## PubDate.day.minutes.poly.2 1.225490 18.08022 FALSE FALSE
## PubDate.day.minutes.poly.4 1.225490 18.08022 FALSE FALSE
## PubDate.last2.log1p 1.375000 51.16350 FALSE FALSE
## PubDate.last32.log1p 1.000000 91.13595 FALSE FALSE
## PubDate.last8.log1p 1.166667 75.15309 FALSE FALSE
## WordCount.log1p 2.315789 24.15799 FALSE FALSE
## WordCount.root2 2.315789 24.15799 FALSE FALSE
## is.cor.y.abs.low interaction.feat
## PubDate.day.minutes.poly.2 FALSE NA
## PubDate.day.minutes.poly.4 FALSE NA
## PubDate.last2.log1p FALSE NA
## PubDate.last32.log1p FALSE NA
## PubDate.last8.log1p FALSE NA
## WordCount.log1p FALSE NA
## WordCount.root2 FALSE NA
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## PubDate.day.minutes.poly.2 8.020999e-64 FALSE NA NA
## PubDate.day.minutes.poly.4 1.523136e-47 FALSE NA NA
## PubDate.last2.log1p 4.200850e-29 FALSE NA NA
## PubDate.last32.log1p 1.048418e-44 FALSE NA NA
## PubDate.last8.log1p 1.021260e-42 FALSE NA NA
## WordCount.log1p 1.576866e-49 FALSE NA NA
## WordCount.root2 4.556481e-30 FALSE NA NA
## max min max.Pplr.fctr.N
## PubDate.day.minutes.poly.2 0.04268445 -0.008758791 0.04268445
## PubDate.day.minutes.poly.4 0.06677441 -0.018327397 0.06543120
## PubDate.last2.log1p 10.91197453 0.693147181 10.91197453
## PubDate.last32.log1p 12.32340669 8.835792367 12.30546086
## PubDate.last8.log1p 11.62246125 6.666956792 11.43577441
## WordCount.log1p 9.29771002 0.000000000 8.81966535
## WordCount.root2 104.46051886 0.000000000 82.24962006
## max.Pplr.fctr.Y min.Pplr.fctr.N min.Pplr.fctr.Y
## PubDate.day.minutes.poly.2 0.04254377 -0.008758791 -0.008758717
## PubDate.day.minutes.poly.4 0.06149053 -0.018327397 -0.018219595
## PubDate.last2.log1p 10.89191288 0.693147181 3.135494216
## PubDate.last32.log1p 12.17840850 9.125653564 9.237663668
## PubDate.last8.log1p 11.39428831 6.666956792 7.199678346
## WordCount.log1p 9.29771002 0.000000000 1.945910149
## WordCount.root2 104.46051886 0.000000000 2.449489743
## max.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.day.minutes.poly.2 0.04268445
## PubDate.day.minutes.poly.4 0.06213811
## PubDate.last2.log1p 10.71916205
## PubDate.last32.log1p 12.17383350
## PubDate.last8.log1p 11.40150216
## WordCount.log1p 7.05961763
## WordCount.root2 34.10278581
## max.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.day.minutes.poly.2 0.04254377
## PubDate.day.minutes.poly.4 0.06610094
## PubDate.last2.log1p 10.84161805
## PubDate.last32.log1p 12.21791228
## PubDate.last8.log1p 11.62246125
## WordCount.log1p 9.14088311
## WordCount.root2 96.58157174
## min.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.day.minutes.poly.2 -0.008758672
## PubDate.day.minutes.poly.4 -0.018326850
## PubDate.last2.log1p 1.386294361
## PubDate.last32.log1p 9.194210990
## PubDate.last8.log1p 6.934397210
## WordCount.log1p 0.000000000
## WordCount.root2 0.000000000
## min.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.day.minutes.poly.2 -0.008758791
## PubDate.day.minutes.poly.4 -0.018240126
## PubDate.last2.log1p 2.995732274
## PubDate.last32.log1p 9.151227107
## PubDate.last8.log1p 7.019296654
## WordCount.log1p 0.000000000
## WordCount.root2 0.000000000
## max.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.day.minutes.poly.2 0.04268445
## PubDate.day.minutes.poly.4 0.06543120
## PubDate.last2.log1p 10.74567954
## PubDate.last32.log1p 12.32340669
## PubDate.last8.log1p 11.27955479
## WordCount.log1p 7.97384438
## WordCount.root2 53.87949517
## max.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.day.minutes.poly.2 0.04254377
## PubDate.day.minutes.poly.4 0.06677441
## PubDate.last2.log1p 10.75436471
## PubDate.last32.log1p 12.30546086
## PubDate.last8.log1p 11.33227851
## WordCount.log1p 8.69232228
## WordCount.root2 77.17512553
## min.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.day.minutes.poly.2 -0.008758791
## PubDate.day.minutes.poly.4 -0.018322678
## PubDate.last2.log1p 2.197224577
## PubDate.last32.log1p 8.862908295
## PubDate.last8.log1p 7.061334367
## WordCount.log1p 0.000000000
## WordCount.root2 0.000000000
## min.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.day.minutes.poly.2 -0.008758672
## PubDate.day.minutes.poly.4 -0.018203392
## PubDate.last2.log1p 3.610917913
## PubDate.last32.log1p 8.835792367
## PubDate.last8.log1p 6.891625897
## WordCount.log1p 1.609437912
## WordCount.root2 2.000000000
## [1] "OOBobs Pplr.fctr.All.X..rcv.glmnet Y: max > max of Train range: 7"
## UniqueID Pplr.fctr.All.X..rcv.glmnet PubDate.day.minutes.poly.1
## 5233 5233 Y -0.016600865
## 6528 6528 Y 0.001809613
## 302 302 Y 0.024722851
## 6435 6435 Y -0.013151170
## 6517 6517 Y 0.020474279
## 3205 3205 Y 0.002898990
## 6521 6521 Y 0.013901702
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## 5233 0.007145007 -0.017000978
## 6528 -0.004472838 0.007219141
## 302 0.051829879 0.066100937
## 6435 0.010843928 -0.010140008
## 6517 0.020825101 0.010136126
## 3205 -0.005881110 0.005605493
## 6521 -0.004412350 -0.013803062
## PubDate.day.minutes.poly.5 PubDate.last16.log1p PubDate.last32.log1p
## 5233 0.013803383 11.76273 11.81975
## 6528 0.006658270 11.95698 12.10953
## 302 0.083442278 10.07706 10.33290
## 6435 -0.001891593 10.33864 10.86697
## 6517 -0.004924177 11.44856 12.21791
## 3205 0.008323368 11.85059 12.00544
## 6521 -0.012191759 11.68539 12.19622
## PubDate.last8.log1p WordCount.nexp
## 5233 11.622461 5.482209e-194
## 6528 11.425547 0.000000e+00
## 302 9.741557 0.000000e+00
## 6435 8.695172 1.000000e+00
## 6517 9.753188 2.750325e-314
## 3205 11.443361 1.026188e-10
## 6521 10.414633 0.000000e+00
## id cor.y
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.1 0.15675348
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.3 0.02798355
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 0.07394139
## PubDate.day.minutes.poly.5 PubDate.day.minutes.poly.5 -0.05592923
## PubDate.last16.log1p PubDate.last16.log1p 0.03845681
## PubDate.last32.log1p PubDate.last32.log1p 0.02254251
## PubDate.last8.log1p PubDate.last8.log1p 0.05657458
## WordCount.nexp WordCount.nexp -0.05320840
## exclude.as.feat cor.y.abs cor.high.X
## PubDate.day.minutes.poly.1 FALSE 0.15675348 <NA>
## PubDate.day.minutes.poly.3 FALSE 0.02798355 <NA>
## PubDate.day.minutes.poly.4 FALSE 0.07394139 <NA>
## PubDate.day.minutes.poly.5 FALSE 0.05592923 <NA>
## PubDate.last16.log1p FALSE 0.03845681 PubDate.last8.log1p
## PubDate.last32.log1p FALSE 0.02254251 <NA>
## PubDate.last8.log1p FALSE 0.05657458 PubDate.last4.log1p
## WordCount.nexp FALSE 0.05320840 <NA>
## freqRatio percentUnique zeroVar nzv
## PubDate.day.minutes.poly.1 1.225490 18.08022 FALSE FALSE
## PubDate.day.minutes.poly.3 1.225490 18.08022 FALSE FALSE
## PubDate.day.minutes.poly.4 1.225490 18.08022 FALSE FALSE
## PubDate.day.minutes.poly.5 1.225490 18.08022 FALSE FALSE
## PubDate.last16.log1p 1.000000 84.50704 FALSE FALSE
## PubDate.last32.log1p 1.000000 91.13595 FALSE FALSE
## PubDate.last8.log1p 1.166667 75.15309 FALSE FALSE
## WordCount.nexp 17.761364 11.32884 FALSE FALSE
## is.cor.y.abs.low interaction.feat
## PubDate.day.minutes.poly.1 FALSE NA
## PubDate.day.minutes.poly.3 FALSE NA
## PubDate.day.minutes.poly.4 FALSE NA
## PubDate.day.minutes.poly.5 FALSE NA
## PubDate.last16.log1p FALSE NA
## PubDate.last32.log1p FALSE NA
## PubDate.last8.log1p FALSE NA
## WordCount.nexp FALSE NA
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## PubDate.day.minutes.poly.1 1.590362e-18 FALSE NA NA
## PubDate.day.minutes.poly.3 9.822405e-52 FALSE NA NA
## PubDate.day.minutes.poly.4 1.523136e-47 FALSE NA NA
## PubDate.day.minutes.poly.5 1.157500e-41 FALSE NA NA
## PubDate.last16.log1p 8.465226e-47 FALSE NA NA
## PubDate.last32.log1p 1.048418e-44 FALSE NA NA
## PubDate.last8.log1p 1.021260e-42 FALSE NA NA
## WordCount.nexp 9.108805e-94 FALSE NA NA
## max min max.Pplr.fctr.N
## PubDate.day.minutes.poly.1 0.02475916 -0.02749464 0.02468654
## PubDate.day.minutes.poly.3 0.05215301 -0.04512497 0.05150779
## PubDate.day.minutes.poly.4 0.06677441 -0.01832740 0.06543120
## PubDate.day.minutes.poly.5 0.08471756 -0.02450918 0.08217780
## PubDate.last16.log1p 11.95698288 8.00068478 11.94531808
## PubDate.last32.log1p 12.32340669 8.83579237 12.30546086
## PubDate.last8.log1p 11.62246125 6.66695679 11.43577441
## WordCount.nexp 1.00000000 0.00000000 1.00000000
## max.Pplr.fctr.Y min.Pplr.fctr.N min.Pplr.fctr.Y
## PubDate.day.minutes.poly.1 0.024468663 -0.02749464 -0.02745833
## PubDate.day.minutes.poly.3 0.049597025 -0.04512497 -0.04482024
## PubDate.day.minutes.poly.4 0.061490534 -0.01832740 -0.01821959
## PubDate.day.minutes.poly.5 0.074814724 -0.02450918 -0.02362780
## PubDate.last16.log1p 11.877603300 8.05452261 8.00068478
## PubDate.last32.log1p 12.178408497 9.12565356 9.23766367
## PubDate.last8.log1p 11.394288315 6.66695679 7.19967835
## WordCount.nexp 0.002478752 0.00000000 0.00000000
## max.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.day.minutes.poly.1 0.02450498
## PubDate.day.minutes.poly.3 0.04991290
## PubDate.day.minutes.poly.4 0.06213811
## PubDate.day.minutes.poly.5 0.07601554
## PubDate.last16.log1p 11.84854019
## PubDate.last32.log1p 12.17383350
## PubDate.last8.log1p 11.40150216
## WordCount.nexp 1.00000000
## max.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.day.minutes.poly.1 0.02472285
## PubDate.day.minutes.poly.3 0.05182988
## PubDate.day.minutes.poly.4 0.06610094
## PubDate.day.minutes.poly.5 0.08344228
## PubDate.last16.log1p 11.95698288
## PubDate.last32.log1p 12.21791228
## PubDate.last8.log1p 11.62246125
## WordCount.nexp 1.00000000
## min.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.day.minutes.poly.1 -0.02749464
## PubDate.day.minutes.poly.3 -0.04512497
## PubDate.day.minutes.poly.4 -0.01832685
## PubDate.day.minutes.poly.5 -0.02450918
## PubDate.last16.log1p 8.23164218
## PubDate.last32.log1p 9.19421099
## PubDate.last8.log1p 6.93439721
## WordCount.nexp 0.00000000
## min.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.day.minutes.poly.1 -0.02745833
## PubDate.day.minutes.poly.3 -0.04482024
## PubDate.day.minutes.poly.4 -0.01824013
## PubDate.day.minutes.poly.5 -0.02362780
## PubDate.last16.log1p 8.29953457
## PubDate.last32.log1p 9.15122711
## PubDate.last8.log1p 7.01929665
## WordCount.nexp 0.00000000
## max.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.day.minutes.poly.1 0.02468654
## PubDate.day.minutes.poly.3 0.05150779
## PubDate.day.minutes.poly.4 0.06543120
## PubDate.day.minutes.poly.5 0.08217780
## PubDate.last16.log1p 11.88113167
## PubDate.last32.log1p 12.32340669
## PubDate.last8.log1p 11.27955479
## WordCount.nexp 1.00000000
## max.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.day.minutes.poly.1 0.02475916
## PubDate.day.minutes.poly.3 0.05215301
## PubDate.day.minutes.poly.4 0.06677441
## PubDate.day.minutes.poly.5 0.08471756
## PubDate.last16.log1p 11.84328621
## PubDate.last32.log1p 12.30546086
## PubDate.last8.log1p 11.33227851
## WordCount.nexp 0.01831564
## min.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.day.minutes.poly.1 -0.02749464
## PubDate.day.minutes.poly.3 -0.04512497
## PubDate.day.minutes.poly.4 -0.01832268
## PubDate.day.minutes.poly.5 -0.02450918
## PubDate.last16.log1p 8.10167775
## PubDate.last32.log1p 8.86290830
## PubDate.last8.log1p 7.06133437
## WordCount.nexp 0.00000000
## min.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.day.minutes.poly.1 -0.02745833
## PubDate.day.minutes.poly.3 -0.04482024
## PubDate.day.minutes.poly.4 -0.01820339
## PubDate.day.minutes.poly.5 -0.02362780
## PubDate.last16.log1p 8.09223941
## PubDate.last32.log1p 8.83579237
## PubDate.last8.log1p 6.89162590
## WordCount.nexp 0.00000000
## [1] "OOBobs Pplr.fctr.All.X..rcv.glmnet N: min < min of Train range: 1"
## UniqueID Pplr.fctr.All.X..rcv.glmnet PubDate.last4.log1p
## 2383 2383 N 4.158883
## id cor.y exclude.as.feat
## PubDate.last4.log1p PubDate.last4.log1p 0.0697764 FALSE
## cor.y.abs cor.high.X freqRatio percentUnique zeroVar
## PubDate.last4.log1p 0.0697764 <NA> 1.125 64.98775 FALSE
## nzv is.cor.y.abs.low interaction.feat
## PubDate.last4.log1p FALSE FALSE NA
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## PubDate.last4.log1p 9.671475e-32 FALSE NA NA
## max min max.Pplr.fctr.N max.Pplr.fctr.Y
## PubDate.last4.log1p 11.24276 4.158883 11.24276 11.2174
## min.Pplr.fctr.N min.Pplr.fctr.Y
## PubDate.last4.log1p 4.382027 5.153292
## max.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.last4.log1p 10.8627
## max.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.last4.log1p 11.02225
## min.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.last4.log1p 4.158883
## min.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.last4.log1p 5.897154
## max.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.last4.log1p 10.98656
## max.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.last4.log1p 11.08721
## min.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.last4.log1p 4.941642
## min.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.last4.log1p 5.442418
## [1] "OOBobs total range outliers: 19"
## [1] "newobs Pplr.fctr.Final..rcv.glmnet N: min < min of Train range: 29"
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.last32.log1p
## 7744 7744 N 9.100860
## 7745 7745 N 9.061608
## 7747 7747 N 9.050172
## 7749 7749 N 8.980550
## 7750 7750 N 8.997518
## 7751 7751 N 8.982310
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.last32.log1p
## 7745 7745 N 9.061608
## 7761 7761 N 9.001100
## 7763 7763 N 9.065315
## 7766 7766 N 9.022081
## 7936 7936 N 8.932741
## 7941 7941 N 8.956351
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.last32.log1p
## 7941 7941 N 8.956351
## 7942 7942 N 8.921991
## 7943 7943 N 8.906393
## 7944 7944 N 8.991064
## 7945 7945 N 9.004054
## 7946 7946 N 9.121509
## id cor.y exclude.as.feat
## PubDate.last32.log1p PubDate.last32.log1p 0.02254251 FALSE
## cor.y.abs cor.high.X freqRatio percentUnique zeroVar
## PubDate.last32.log1p 0.02254251 <NA> 1 91.13595 FALSE
## nzv is.cor.y.abs.low interaction.feat
## PubDate.last32.log1p FALSE FALSE NA
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## PubDate.last32.log1p 1.048418e-44 FALSE NA NA
## max min max.Pplr.fctr.N max.Pplr.fctr.Y
## PubDate.last32.log1p 12.32341 8.835792 12.30546 12.21791
## min.Pplr.fctr.N min.Pplr.fctr.Y
## PubDate.last32.log1p 9.125654 9.17699
## max.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.last32.log1p 12.17383
## max.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.last32.log1p 12.21791
## min.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.last32.log1p 9.194211
## min.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.last32.log1p 9.151227
## max.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.last32.log1p 12.32341
## max.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.last32.log1p 12.30546
## min.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.last32.log1p 8.862908
## min.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.last32.log1p 8.835792
## [1] "newobs Pplr.fctr.Final..rcv.glmnet N: max > max of Train range: 1189"
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.juliandate
## 6533 6533 N 335
## 6540 6540 N 335
## 6541 6541 N 335
## 6542 6542 N 335
## 6543 6543 N 335
## 6545 6545 N 335
## PubDate.last32.log1p
## 6533 10.134321
## 6540 9.418817
## 6541 9.442800
## 6542 9.391411
## 6543 9.400878
## 6545 9.402860
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.juliandate
## 6717 6717 N 336
## 6880 6880 N 338
## 7332 7332 N 345
## 7537 7537 N 349
## 7724 7724 N 351
## 8062 8062 N 356
## PubDate.last32.log1p
## 6717 10.684760
## 6880 10.930371
## 7332 9.601842
## 7537 10.000796
## 7724 9.325097
## 8062 10.218590
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.juliandate
## 8394 8394 N 365
## 8395 8395 N 365
## 8396 8396 N 365
## 8398 8398 N 365
## 8400 8400 N 365
## 8401 8401 N 365
## PubDate.last32.log1p
## 8394 11.14224
## 8395 11.14428
## 8396 11.11651
## 8398 11.09410
## 8400 11.07855
## 8401 11.05398
## id cor.y exclude.as.feat
## PubDate.juliandate PubDate.juliandate 0.01436107 FALSE
## PubDate.last32.log1p PubDate.last32.log1p 0.02254251 FALSE
## cor.y.abs cor.high.X freqRatio percentUnique
## PubDate.juliandate 0.01436107 PubDate.month.fctr 1.03252 1.393141
## PubDate.last32.log1p 0.02254251 <NA> 1.00000 91.135946
## zeroVar nzv is.cor.y.abs.low interaction.feat
## PubDate.juliandate FALSE FALSE FALSE NA
## PubDate.last32.log1p FALSE FALSE FALSE NA
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## PubDate.juliandate 1.389406e-35 FALSE NA NA
## PubDate.last32.log1p 1.048418e-44 FALSE NA NA
## max min max.Pplr.fctr.N max.Pplr.fctr.Y
## PubDate.juliandate 365.00000 244.000000 334.00000 334.00000
## PubDate.last32.log1p 12.32341 8.835792 12.30546 12.21791
## min.Pplr.fctr.N min.Pplr.fctr.Y
## PubDate.juliandate 244.000000 244.00000
## PubDate.last32.log1p 9.125654 9.17699
## max.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.juliandate 332.00000
## PubDate.last32.log1p 12.17383
## max.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.juliandate 334.00000
## PubDate.last32.log1p 12.21791
## min.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.juliandate 244.000000
## PubDate.last32.log1p 9.194211
## min.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.juliandate 244.000000
## PubDate.last32.log1p 9.151227
## max.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.juliandate 365.00000
## PubDate.last32.log1p 12.32341
## max.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.juliandate 365.00000
## PubDate.last32.log1p 12.30546
## min.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.juliandate 335.000000
## PubDate.last32.log1p 8.862908
## min.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.juliandate 335.000000
## PubDate.last32.log1p 8.835792
## [1] "newobs Pplr.fctr.Final..rcv.glmnet Y: min < min of Train range: 10"
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.last32.log1p
## 6635 6635 Y 10.171451
## 6636 6636 Y 10.162461
## 7742 7742 Y 9.087268
## 7743 7743 Y 9.096836
## 7746 7746 Y 9.060215
## 7748 7748 Y 9.049115
## 7759 7759 Y 9.069813
## 7938 7938 Y 8.860641
## 7939 7939 Y 8.835792
## 8217 8217 Y 11.168659
## PubDate.last8.log1p WordCount.log1p WordCount.root2
## 6635 7.156177 7.609862 44.91102
## 6636 6.891626 6.853299 30.75711
## 7742 7.708860 7.565275 43.92038
## 7743 7.748891 6.144186 21.56386
## 7746 7.610358 6.363028 24.06242
## 7748 7.536897 5.455321 15.26434
## 7759 7.826842 6.809039 30.08322
## 7938 7.494430 6.543912 26.34388
## 7939 7.101676 6.115892 21.26029
## 8217 9.528794 1.609438 2.00000
## id cor.y exclude.as.feat
## PubDate.last32.log1p PubDate.last32.log1p 0.02254251 FALSE
## PubDate.last8.log1p PubDate.last8.log1p 0.05657458 FALSE
## WordCount.log1p WordCount.log1p 0.25431963 FALSE
## WordCount.root2 WordCount.root2 0.29212068 FALSE
## cor.y.abs cor.high.X freqRatio
## PubDate.last32.log1p 0.02254251 <NA> 1.000000
## PubDate.last8.log1p 0.05657458 PubDate.last4.log1p 1.166667
## WordCount.log1p 0.25431963 WordCount.root2 2.315789
## WordCount.root2 0.29212068 <NA> 2.315789
## percentUnique zeroVar nzv is.cor.y.abs.low
## PubDate.last32.log1p 91.13595 FALSE FALSE FALSE
## PubDate.last8.log1p 75.15309 FALSE FALSE FALSE
## WordCount.log1p 24.15799 FALSE FALSE FALSE
## WordCount.root2 24.15799 FALSE FALSE FALSE
## interaction.feat shapiro.test.p.value rsp_var_raw
## PubDate.last32.log1p NA 1.048418e-44 FALSE
## PubDate.last8.log1p NA 1.021260e-42 FALSE
## WordCount.log1p NA 1.576866e-49 FALSE
## WordCount.root2 NA 4.556481e-30 FALSE
## id_var rsp_var max min max.Pplr.fctr.N
## PubDate.last32.log1p NA NA 12.32341 8.835792 12.305461
## PubDate.last8.log1p NA NA 11.62246 6.666957 11.622461
## WordCount.log1p NA NA 9.29771 0.000000 8.819665
## WordCount.root2 NA NA 104.46052 0.000000 82.249620
## max.Pplr.fctr.Y min.Pplr.fctr.N min.Pplr.fctr.Y
## PubDate.last32.log1p 12.21791 9.125654 9.176990
## PubDate.last8.log1p 11.44336 6.666957 7.187657
## WordCount.log1p 9.29771 0.000000 1.945910
## WordCount.root2 104.46052 0.000000 2.449490
## max.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.last32.log1p 12.173834
## PubDate.last8.log1p 11.401502
## WordCount.log1p 7.059618
## WordCount.root2 34.102786
## max.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.last32.log1p 12.217912
## PubDate.last8.log1p 11.622461
## WordCount.log1p 9.140883
## WordCount.root2 96.581572
## min.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.last32.log1p 9.194211
## PubDate.last8.log1p 6.934397
## WordCount.log1p 0.000000
## WordCount.root2 0.000000
## min.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.last32.log1p 9.151227
## PubDate.last8.log1p 7.019297
## WordCount.log1p 0.000000
## WordCount.root2 0.000000
## max.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.last32.log1p 12.323407
## PubDate.last8.log1p 11.279555
## WordCount.log1p 7.973844
## WordCount.root2 53.879495
## max.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.last32.log1p 12.305461
## PubDate.last8.log1p 11.332279
## WordCount.log1p 8.692322
## WordCount.root2 77.175126
## min.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.last32.log1p 8.862908
## PubDate.last8.log1p 7.061334
## WordCount.log1p 0.000000
## WordCount.root2 0.000000
## min.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.last32.log1p 8.835792
## PubDate.last8.log1p 6.891626
## WordCount.log1p 1.609438
## WordCount.root2 2.000000
## [1] "newobs Pplr.fctr.Final..rcv.glmnet Y: max > max of Train range: 681"
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.day.minutes.poly.1
## 6534 6534 Y 0.02047428
## 6535 6535 Y 0.02043797
## 6536 6536 Y 0.01840446
## 6537 6537 Y 0.01437377
## 6538 6538 Y 0.01408327
## 6539 6539 Y 0.01390170
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## 6534 0.020825101 0.010136126
## 6535 0.020614888 0.009828175
## 6536 0.010192448 -0.003724687
## 6537 -0.003371562 -0.013710045
## 6538 -0.004024505 -0.013788116
## 6539 -0.004412350 -0.013803062
## PubDate.day.minutes.poly.5 PubDate.juliandate PubDate.last32.log1p
## 6534 -0.004924177 335 10.036094
## 6535 -0.005269034 335 10.053458
## 6536 -0.017201046 335 9.939434
## 6537 -0.013755146 335 9.564863
## 6538 -0.012806242 335 9.542733
## 6539 -0.012191759 335 9.572898
## WordCount.nexp
## 6534 4.609768e-243
## 6535 0.000000e+00
## 6536 0.000000e+00
## 6537 3.128062e-93
## 6538 0.000000e+00
## 6539 0.000000e+00
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.day.minutes.poly.1
## 6549 6549 Y 0.0103067569
## 6568 6568 Y 0.0058403094
## 6859 6859 Y -0.0003328293
## 8074 8074 Y -0.0039277748
## 8137 8137 Y -0.0057434038
## 8179 8179 Y -0.0037098993
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## 6549 -0.009159941 -0.0099955355
## 6568 -0.008760997 -0.0002028669
## 6859 -0.001377328 0.0092461622
## 8074 0.004086564 0.0087598734
## 8137 0.006605125 0.0066524422
## 8179 0.003766018 0.0089316619
## PubDate.day.minutes.poly.5 PubDate.juliandate PubDate.last32.log1p
## 6549 0.0006942245 335 9.471242
## 6568 0.0095286285 335 9.661734
## 6859 0.0021075916 338 9.886443
## 8074 -0.0065295954 356 10.655847
## 8137 -0.0098074102 357 10.956440
## 8179 -0.0060564299 358 11.239304
## WordCount.nexp
## 6549 0.000000e+00
## 6568 5.688906e-247
## 6859 7.149792e-142
## 8074 0.000000e+00
## 8137 0.000000e+00
## 8179 0.000000e+00
## UniqueID Pplr.fctr.Final..rcv.glmnet PubDate.day.minutes.poly.1
## 8386 8386 Y -0.004762964
## 8391 8391 Y -0.007559033
## 8392 8392 Y -0.007885846
## 8397 8397 Y -0.012134418
## 8399 8399 Y -0.014204235
## 8402 8402 Y -0.027458327
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## 8386 0.005283252 0.007937277
## 8391 0.008724731 0.003454984
## 8392 0.009050092 0.002780751
## 8397 0.011101808 -0.007579609
## 8399 0.010203360 -0.012625915
## 8402 -0.044820236 0.033658267
## PubDate.day.minutes.poly.5 PubDate.juliandate PubDate.last32.log1p
## 8386 -0.008201720 365 11.19043
## 8391 -0.011437989 365 11.15722
## 8392 -0.011513182 365 11.15937
## 8397 -0.005346258 365 11.11095
## 8399 0.002370447 365 11.10274
## 8402 -0.023627798 365 10.79296
## WordCount.nexp
## 8386 0
## 8391 0
## 8392 0
## 8397 0
## 8399 0
## 8402 0
## id cor.y
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.1 0.15675348
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.3 0.02798355
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 0.07394139
## PubDate.day.minutes.poly.5 PubDate.day.minutes.poly.5 -0.05592923
## PubDate.juliandate PubDate.juliandate 0.01436107
## PubDate.last32.log1p PubDate.last32.log1p 0.02254251
## WordCount.nexp WordCount.nexp -0.05320840
## exclude.as.feat cor.y.abs cor.high.X
## PubDate.day.minutes.poly.1 FALSE 0.15675348 <NA>
## PubDate.day.minutes.poly.3 FALSE 0.02798355 <NA>
## PubDate.day.minutes.poly.4 FALSE 0.07394139 <NA>
## PubDate.day.minutes.poly.5 FALSE 0.05592923 <NA>
## PubDate.juliandate FALSE 0.01436107 PubDate.month.fctr
## PubDate.last32.log1p FALSE 0.02254251 <NA>
## WordCount.nexp FALSE 0.05320840 <NA>
## freqRatio percentUnique zeroVar nzv
## PubDate.day.minutes.poly.1 1.22549 18.080220 FALSE FALSE
## PubDate.day.minutes.poly.3 1.22549 18.080220 FALSE FALSE
## PubDate.day.minutes.poly.4 1.22549 18.080220 FALSE FALSE
## PubDate.day.minutes.poly.5 1.22549 18.080220 FALSE FALSE
## PubDate.juliandate 1.03252 1.393141 FALSE FALSE
## PubDate.last32.log1p 1.00000 91.135946 FALSE FALSE
## WordCount.nexp 17.76136 11.328843 FALSE FALSE
## is.cor.y.abs.low interaction.feat
## PubDate.day.minutes.poly.1 FALSE NA
## PubDate.day.minutes.poly.3 FALSE NA
## PubDate.day.minutes.poly.4 FALSE NA
## PubDate.day.minutes.poly.5 FALSE NA
## PubDate.juliandate FALSE NA
## PubDate.last32.log1p FALSE NA
## WordCount.nexp FALSE NA
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## PubDate.day.minutes.poly.1 1.590362e-18 FALSE NA NA
## PubDate.day.minutes.poly.3 9.822405e-52 FALSE NA NA
## PubDate.day.minutes.poly.4 1.523136e-47 FALSE NA NA
## PubDate.day.minutes.poly.5 1.157500e-41 FALSE NA NA
## PubDate.juliandate 1.389406e-35 FALSE NA NA
## PubDate.last32.log1p 1.048418e-44 FALSE NA NA
## WordCount.nexp 9.108805e-94 FALSE NA NA
## max min max.Pplr.fctr.N
## PubDate.day.minutes.poly.1 0.02475916 -0.02749464 0.02472285
## PubDate.day.minutes.poly.3 0.05215301 -0.04512497 0.05182988
## PubDate.day.minutes.poly.4 0.06677441 -0.01832740 0.06610094
## PubDate.day.minutes.poly.5 0.08471756 -0.02450918 0.08344228
## PubDate.juliandate 365.00000000 244.00000000 334.00000000
## PubDate.last32.log1p 12.32340669 8.83579237 12.30546086
## WordCount.nexp 1.00000000 0.00000000 1.00000000
## max.Pplr.fctr.Y min.Pplr.fctr.N min.Pplr.fctr.Y
## PubDate.day.minutes.poly.1 2.446866e-02 -0.02749464 -0.02745833
## PubDate.day.minutes.poly.3 4.959703e-02 -0.04512497 -0.04482024
## PubDate.day.minutes.poly.4 6.149053e-02 -0.01832740 -0.01821959
## PubDate.day.minutes.poly.5 7.481472e-02 -0.02450918 -0.02362780
## PubDate.juliandate 3.340000e+02 244.00000000 244.00000000
## PubDate.last32.log1p 1.221791e+01 9.12565356 9.17699039
## WordCount.nexp 2.478752e-03 0.00000000 0.00000000
## max.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.day.minutes.poly.1 0.02450498
## PubDate.day.minutes.poly.3 0.04991290
## PubDate.day.minutes.poly.4 0.06213811
## PubDate.day.minutes.poly.5 0.07601554
## PubDate.juliandate 332.00000000
## PubDate.last32.log1p 12.17383350
## WordCount.nexp 1.00000000
## max.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.day.minutes.poly.1 0.02472285
## PubDate.day.minutes.poly.3 0.05182988
## PubDate.day.minutes.poly.4 0.06610094
## PubDate.day.minutes.poly.5 0.08344228
## PubDate.juliandate 334.00000000
## PubDate.last32.log1p 12.21791228
## WordCount.nexp 1.00000000
## min.Pplr.fctr.All.X..rcv.glmnet.N
## PubDate.day.minutes.poly.1 -0.02749464
## PubDate.day.minutes.poly.3 -0.04512497
## PubDate.day.minutes.poly.4 -0.01832685
## PubDate.day.minutes.poly.5 -0.02450918
## PubDate.juliandate 244.00000000
## PubDate.last32.log1p 9.19421099
## WordCount.nexp 0.00000000
## min.Pplr.fctr.All.X..rcv.glmnet.Y
## PubDate.day.minutes.poly.1 -0.02745833
## PubDate.day.minutes.poly.3 -0.04482024
## PubDate.day.minutes.poly.4 -0.01824013
## PubDate.day.minutes.poly.5 -0.02362780
## PubDate.juliandate 244.00000000
## PubDate.last32.log1p 9.15122711
## WordCount.nexp 0.00000000
## max.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.day.minutes.poly.1 0.02468654
## PubDate.day.minutes.poly.3 0.05150779
## PubDate.day.minutes.poly.4 0.06543120
## PubDate.day.minutes.poly.5 0.08217780
## PubDate.juliandate 365.00000000
## PubDate.last32.log1p 12.32340669
## WordCount.nexp 1.00000000
## max.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.day.minutes.poly.1 0.02475916
## PubDate.day.minutes.poly.3 0.05215301
## PubDate.day.minutes.poly.4 0.06677441
## PubDate.day.minutes.poly.5 0.08471756
## PubDate.juliandate 365.00000000
## PubDate.last32.log1p 12.30546086
## WordCount.nexp 0.01831564
## min.Pplr.fctr.Final..rcv.glmnet.N
## PubDate.day.minutes.poly.1 -0.02749464
## PubDate.day.minutes.poly.3 -0.04512497
## PubDate.day.minutes.poly.4 -0.01832268
## PubDate.day.minutes.poly.5 -0.02450918
## PubDate.juliandate 335.00000000
## PubDate.last32.log1p 8.86290830
## WordCount.nexp 0.00000000
## min.Pplr.fctr.Final..rcv.glmnet.Y
## PubDate.day.minutes.poly.1 -0.02745833
## PubDate.day.minutes.poly.3 -0.04482024
## PubDate.day.minutes.poly.4 -0.01820339
## PubDate.day.minutes.poly.5 -0.02362780
## PubDate.juliandate 335.00000000
## PubDate.last32.log1p 8.83579237
## WordCount.nexp 0.00000000
## [1] "newobs total range outliers: 1870"## numeric(0)## [1] "glb_sel_mdl_id: All.X##rcv#glmnet"## [1] "glb_fin_mdl_id: Final##rcv#glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1.cp.0###rpart
## 0 0## max.Accuracy.OOB max.AUCROCR.OOB
## Max.cor.Y##rcv#rpart 0.8200231 0.5892132
## Max.cor.Y.Time.Poly##rcv#glmnet 0.7754630 0.8049472
## Interact.High.cor.Y##rcv#glmnet 0.7743056 0.7992251
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.7673611 0.7773858
## Max.cor.Y.rcv.1X1###glmnet 0.7604167 0.8116126
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.7604167 0.8114863
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.7604167 0.8114863
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.7604167 0.8114863
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.7575231 0.8067975
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.7575231 0.8067975
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.7575231 0.8067975
## Max.cor.Y.Time.Lag##rcv#glmnet 0.6464120 0.8118709
## Low.cor.X##rcv#glmnet 0.6377315 0.8150026
## All.X##rcv#glmnet 0.6261574 0.8157921
## MFO###myMFO_classfr 0.1331019 0.5000000
## Random###myrandom_classfr 0.1331019 0.4857956
## Final##rcv#glmnet NA NA
## max.AUCpROC.OOB max.Accuracy.fit
## Max.cor.Y##rcv#rpart 0.5870523 0.9296422
## Max.cor.Y.Time.Poly##rcv#glmnet 0.5965780 0.9322790
## Interact.High.cor.Y##rcv#glmnet 0.5957392 0.9315850
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.6174697 0.9381765
## Max.cor.Y.rcv.1X1###glmnet 0.5962443 0.9329725
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.5962443 0.9333905
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.5962443 0.9331818
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.5962443 0.9331816
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.5962443 0.9335973
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.5962443 0.9333193
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.5962443 0.9332218
## Max.cor.Y.Time.Lag##rcv#glmnet 0.5928717 0.9281861
## Low.cor.X##rcv#glmnet 0.5876850 0.9261046
## All.X##rcv#glmnet 0.5876850 0.9263124
## MFO###myMFO_classfr 0.5000000 0.1796420
## Random###myrandom_classfr 0.5125675 0.1796420
## Final##rcv#glmnet NA 0.9063073
## opt.prob.threshold.fit
## Max.cor.Y##rcv#rpart 0.6
## Max.cor.Y.Time.Poly##rcv#glmnet 0.4
## Interact.High.cor.Y##rcv#glmnet 0.4
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.4
## Max.cor.Y.rcv.1X1###glmnet 0.5
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.5
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.5
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.5
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.4
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.4
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.4
## Max.cor.Y.Time.Lag##rcv#glmnet 0.3
## Low.cor.X##rcv#glmnet 0.3
## All.X##rcv#glmnet 0.3
## MFO###myMFO_classfr 0.1
## Random###myrandom_classfr 0.1
## Final##rcv#glmnet 0.2
## opt.prob.threshold.OOB
## Max.cor.Y##rcv#rpart 0.6
## Max.cor.Y.Time.Poly##rcv#glmnet 0.1
## Interact.High.cor.Y##rcv#glmnet 0.1
## Max.cor.Y.rcv.1X1.cp.0###rpart 0.1
## Max.cor.Y.rcv.1X1###glmnet 0.1
## Max.cor.Y.rcv.5X3##rcv#glmnet 0.1
## Max.cor.Y.rcv.5X1##rcv#glmnet 0.1
## Max.cor.Y.rcv.5X5##rcv#glmnet 0.1
## Max.cor.Y.rcv.3X1##rcv#glmnet 0.1
## Max.cor.Y.rcv.3X3##rcv#glmnet 0.1
## Max.cor.Y.rcv.3X5##rcv#glmnet 0.1
## Max.cor.Y.Time.Lag##rcv#glmnet 0.1
## Low.cor.X##rcv#glmnet 0.1
## All.X##rcv#glmnet 0.1
## MFO###myMFO_classfr 0.1
## Random###myrandom_classfr 0.1
## Final##rcv#glmnet NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 874 624
## Y 22 208
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## OpEd#Opnn# 0.090965862 0.0515046296 0.087700535
## #Opnn#ThPblcEdtr 0.003330558 0.0023148148 0.005347594
## Styls#U.S.# 0.026436303 0.0289351852 0.032620321
## Bsnss#Crsswrds/Gms# 0.021856786 0.0104166667 0.022459893
## Scnc#Hlth# 0.030807660 0.0277777778 0.030481283
## Bsnss#Tchnlgy# 0.044338052 0.0729166667 0.060962567
## ## 0.190049958 0.2146990741 0.182887701
## Bsnss#BsnssDy#Dlbk 0.130932556 0.1869212963 0.162566845
## Mtr#N.Y./Rgn# 0.026644463 0.0405092593 0.035828877
## Cltr#Arts# 0.101998335 0.1070601852 0.093048128
## #Opnn#RmFrDbt 0.008742714 0.0115740741 0.010695187
## Styls##Fshn 0.021648626 0.0086805556 0.008021390
## Bsnss#BsnssDy#SmllBsnss 0.020815987 0.0231481481 0.021925134
## myOthr 0.006869276 0.0028935185 0.002673797
## Trvl#Trvl# 0.017277269 0.0196759259 0.018716578
## Cltr## NA 0.0005787037 0.037433155
## Frgn#Wrld#AsPcfc 0.031223980 0.0306712963 0.029946524
## #Mltmd# 0.019150708 0.0283564815 0.027807487
## TStyl## 0.129683597 0.0584490741 0.056149733
## #U.S.#Edctn 0.050582848 0.0474537037 0.047593583
## Frgn#Wrld# 0.026644463 0.0254629630 0.025133690
## .n.Fit .n.New.N .n.New.Y .n.OOB .n.Trn.N .n.Trn.Y
## OpEd#Opnn# 437 NA 164 89 117 409
## #Opnn#ThPblcEdtr 16 NA 10 4 4 16
## Styls#U.S.# 127 NA 61 50 77 100
## Bsnss#Crsswrds/Gms# 105 NA 42 18 20 103
## Scnc#Hlth# 148 NA 57 48 74 122
## Bsnss#Tchnlgy# 213 34 80 126 288 51
## ## 913 262 80 371 1169 115
## Bsnss#BsnssDy#Dlbk 629 201 103 323 864 88
## Mtr#N.Y./Rgn# 128 36 31 70 181 17
## Cltr#Arts# 490 157 17 185 625 50
## #Opnn#RmFrDbt 42 20 NA 20 61 1
## Styls##Fshn 104 15 NA 15 118 1
## Bsnss#BsnssDy#SmllBsnss 100 36 5 40 135 5
## myOthr 33 5 NA 5 38 NA
## Trvl#Trvl# 83 35 NA 34 116 1
## Cltr## NA 47 23 1 1 NA
## Frgn#Wrld#AsPcfc 150 51 5 53 200 3
## #Mltmd# 92 52 NA 49 139 2
## TStyl## 623 104 1 101 715 9
## #U.S.#Edctn 243 87 2 82 325 NA
## Frgn#Wrld# 128 47 NA 44 172 NA
## .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## OpEd#Opnn# 164 437 164 526 0.52330093
## #Opnn#ThPblcEdtr 10 16 10 20 0.50201689
## Styls#U.S.# 61 127 61 177 0.47265683
## Bsnss#Crsswrds/Gms# 42 105 42 123 0.46617390
## Scnc#Hlth# 57 148 57 196 0.46081076
## Bsnss#Tchnlgy# 114 213 114 339 0.23596749
## ## 342 913 342 1284 0.21028224
## Bsnss#BsnssDy#Dlbk 304 629 304 952 0.20486485
## Mtr#N.Y./Rgn# 67 128 67 198 0.19249438
## Cltr#Arts# 174 490 174 675 0.18753758
## #Opnn#RmFrDbt 20 42 20 62 0.18712633
## Styls##Fshn 15 104 15 119 0.14376823
## Bsnss#BsnssDy#SmllBsnss 41 100 41 140 0.14054271
## myOthr 5 33 5 38 0.11351975
## Trvl#Trvl# 35 83 35 117 0.10662343
## Cltr## 70 NA 70 1 0.10322006
## Frgn#Wrld#AsPcfc 56 150 56 203 0.10088812
## #Mltmd# 52 92 52 141 0.09869284
## TStyl## 105 623 105 724 0.09409437
## #U.S.#Edctn 89 243 89 325 0.07342928
## Frgn#Wrld# 47 128 47 172 0.07272040
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## OpEd#Opnn# 0.38883472 NA 0.33295283
## #Opnn#ThPblcEdtr 0.45394245 NA 0.32096412
## Styls#U.S.# 0.49068821 NA 0.45281920
## Bsnss#Crsswrds/Gms# 0.35845239 NA 0.23739811
## Scnc#Hlth# 0.45418853 NA 0.38566104
## Bsnss#Tchnlgy# 0.22005479 NA 0.23159760
## ## 0.14540647 NA 0.13629887
## Bsnss#BsnssDy#Dlbk 0.15383991 NA 0.15219558
## Mtr#N.Y./Rgn# 0.15574134 NA 0.15274373
## Cltr#Arts# 0.12292240 NA 0.10912086
## #Opnn#RmFrDbt 0.15566721 NA 0.05718416
## Styls##Fshn 0.08609432 NA 0.03338503
## Bsnss#BsnssDy#SmllBsnss 0.13032416 NA 0.08239213
## myOthr 0.11282519 NA 0.02825053
## Trvl#Trvl# 0.08193916 NA 0.03226070
## Cltr## NA NA 0.07220822
## Frgn#Wrld#AsPcfc 0.10382572 NA 0.04236734
## #Mltmd# 0.09099175 NA 0.04168488
## TStyl## 0.06990527 NA 0.02853194
## #U.S.#Edctn 0.06377772 NA 0.01167100
## Frgn#Wrld# 0.06994962 NA 0.01407013
## err.abs.OOB.sum err.abs.fit.sum err.abs.new.sum
## OpEd#Opnn# 46.5737830 169.920774 NA
## #Opnn#ThPblcEdtr 2.0080675 7.263079 NA
## Styls#U.S.# 23.6328413 62.317403 NA
## Bsnss#Crsswrds/Gms# 8.3911301 37.637501 NA
## Scnc#Hlth# 22.1189167 67.219902 NA
## Bsnss#Tchnlgy# 29.7319043 46.871671 NA
## ## 78.0147094 132.756111 NA
## Bsnss#BsnssDy#Dlbk 66.1713471 96.765300 NA
## Mtr#N.Y./Rgn# 13.4746063 19.934891 NA
## Cltr#Arts# 34.6944525 60.231977 NA
## #Opnn#RmFrDbt 3.7425266 6.538023 NA
## Styls##Fshn 2.1565235 8.953809 NA
## Bsnss#BsnssDy#SmllBsnss 5.6217083 13.032416 NA
## myOthr 0.5675988 3.723231 NA
## Trvl#Trvl# 3.6251966 6.800950 NA
## Cltr## 0.1032201 NA NA
## Frgn#Wrld#AsPcfc 5.3470704 15.573858 NA
## #Mltmd# 4.8359493 8.371241 NA
## TStyl## 9.5035315 43.550981 NA
## #U.S.#Edctn 6.0212009 15.497987 NA
## Frgn#Wrld# 3.1996976 8.953551 NA
## err.abs.trn.sum
## OpEd#Opnn# 175.13319010
## #Opnn#ThPblcEdtr 6.41928249
## Styls#U.S.# 80.14899777
## Bsnss#Crsswrds/Gms# 29.19996741
## Scnc#Hlth# 75.58956367
## Bsnss#Tchnlgy# 78.51158544
## ## 175.00774839
## Bsnss#BsnssDy#Dlbk 144.89018795
## Mtr#N.Y./Rgn# 30.24325893
## Cltr#Arts# 73.65658273
## #Opnn#RmFrDbt 3.54541784
## Styls##Fshn 3.97281808
## Bsnss#BsnssDy#SmllBsnss 11.53489794
## myOthr 1.07352023
## Trvl#Trvl# 3.77450215
## Cltr## 0.07220822
## Frgn#Wrld#AsPcfc 8.60057057
## #Mltmd# 5.87756799
## TStyl## 20.65712219
## #U.S.#Edctn 3.79307552
## Frgn#Wrld# 2.42006229
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## NA 1.000000 1.000000 NA
## .n.New.N .n.New.Y .n.OOB .n.Trn.N
## NA NA 1728.000000 5439.000000
## .n.Trn.Y .n.Tst .n.fit .n.new
## NA 1870.000000 NA 1870.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 6532.000000 4.690731 NA NA
## err.abs.trn.mean err.abs.OOB.sum err.abs.fit.sum err.abs.new.sum
## 2.955758 369.535982 NA NA
## err.abs.trn.sum
## 934.122128## All.X__rcv_glmnet.imp
## PubDate.day.minutes.poly.1 100.000000
## PubDate.day.minutes.poly.4 46.118345
## NDSSName.my.fctrOpEd#Opnn# 29.608813
## NDSSName.my.fctrBsnss#Crsswrds/Gms# 27.135657
## NDSSName.my.fctrScnc#Hlth# 24.964890
## NDSSName.my.fctr#Opnn#ThPblcEdtr 24.849247
## NDSSName.my.fctrStyls#U.S.# 23.449567
## PubDate.day.minutes.poly.2 22.879214
## WordCount.log1p 7.599061
## PubDate.wkend 7.555716
## PubDate.hour.fctr(15.3,23] 6.568901
## WordCount.root2 6.405483
## PubDate.last4.log1p 6.403279
## PubDate.last2.log1p 6.307833
## PubDate.last8.log1p 6.231915
## PubDate.day.minutes.poly.3 6.181073
## NDSSName.my.fctrBsnss#Tchnlgy# 6.181073
## PubDate.hour.fctr(7.67,15.3] 6.181073
## PubDate.wkday.fctr1 6.181073
## PubDate.last16.log1p 6.181073
## PubDate.date.fctr(7,13] 6.181073
## PubDate.date.fctr(25,31] 6.181073
## .rnorm 6.181073
## NDSSName.my.fctrCltr## 6.181073
## NDSSName.my.fctrMtr#N.Y./Rgn# 6.181073
## PubDate.date.fctr(19,25] 6.181073
## PubDate.day.minutes.poly.5 6.181073
## PubDate.last32.log1p 6.181073
## PubDate.minute.fctr(14.8,29.5] 6.181073
## PubDate.minute.fctr(44.2,59.1] 6.181073
## PubDate.month.fctr10 6.181073
## PubDate.month.fctr12 6.181073
## PubDate.second.fctr(14.8,29.5] 6.181073
## PubDate.second.fctr(29.5,44.2] 6.181073
## PubDate.wkday.fctr3 6.181073
## PubDate.wkday.fctr4 6.181073
## WordCount.nexp 6.181073
## PubDate.juliandate 6.181073
## PubDate.wkday.fctr2 6.181073
## PubDate.date.fctr(13,19] 6.181073
## PubDate.month.fctr11 6.181073
## PubDate.second.fctr(44.2,59.1] 6.181073
## PubDate.wkday.fctr5 6.181073
## PubDate.minute.fctr(29.5,44.2] 6.181073
## PubDate.wkday.fctr6 6.181073
## NDSSName.my.fctrTrvl#Trvl# 4.901316
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss 4.791503
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk 4.575817
## NDSSName.my.fctrCltr#Arts# 4.494666
## Final__rcv_glmnet.imp
## PubDate.day.minutes.poly.1 78.09046
## PubDate.day.minutes.poly.4 21.36267
## NDSSName.my.fctrOpEd#Opnn# 29.73570
## NDSSName.my.fctrBsnss#Crsswrds/Gms# 27.53840
## NDSSName.my.fctrScnc#Hlth# 25.24514
## NDSSName.my.fctr#Opnn#ThPblcEdtr 27.29376
## NDSSName.my.fctrStyls#U.S.# 24.24782
## PubDate.day.minutes.poly.2 100.00000
## WordCount.log1p 15.19157
## PubDate.wkend 15.37916
## PubDate.hour.fctr(15.3,23] 13.49353
## WordCount.root2 13.72215
## PubDate.last4.log1p 13.49353
## PubDate.last2.log1p 13.55706
## PubDate.last8.log1p 13.49353
## PubDate.day.minutes.poly.3 28.57451
## NDSSName.my.fctrBsnss#Tchnlgy# 15.43897
## PubDate.hour.fctr(7.67,15.3] 14.61230
## PubDate.wkday.fctr1 13.91542
## PubDate.last16.log1p 13.80029
## PubDate.date.fctr(7,13] 13.67140
## PubDate.date.fctr(25,31] 13.63428
## .rnorm 13.49353
## NDSSName.my.fctrCltr## 13.49353
## NDSSName.my.fctrMtr#N.Y./Rgn# 13.49353
## PubDate.date.fctr(19,25] 13.49353
## PubDate.day.minutes.poly.5 13.49353
## PubDate.last32.log1p 13.49353
## PubDate.minute.fctr(14.8,29.5] 13.49353
## PubDate.minute.fctr(44.2,59.1] 13.49353
## PubDate.month.fctr10 13.49353
## PubDate.month.fctr12 13.49353
## PubDate.second.fctr(14.8,29.5] 13.49353
## PubDate.second.fctr(29.5,44.2] 13.49353
## PubDate.wkday.fctr3 13.49353
## PubDate.wkday.fctr4 13.49353
## WordCount.nexp 13.49353
## PubDate.juliandate 13.49350
## PubDate.wkday.fctr2 13.31814
## PubDate.date.fctr(13,19] 13.26769
## PubDate.month.fctr11 13.16393
## PubDate.second.fctr(44.2,59.1] 13.11401
## PubDate.wkday.fctr5 12.82054
## PubDate.minute.fctr(29.5,44.2] 12.78565
## PubDate.wkday.fctr6 12.51262
## NDSSName.my.fctrTrvl#Trvl# 10.36195
## NDSSName.my.fctrBsnss#BsnssDy#SmllBsnss 10.18924
## NDSSName.my.fctrBsnss#BsnssDy#Dlbk 12.35507
## NDSSName.my.fctrCltr#Arts# 12.63313## [1] "glbObsNew prediction stats:"##
## N Y
## 1189 681## label step_major step_minor label_minor bgn end
## 16 predict.data.new 8 0 0 358.247 379.494
## 17 display.session.info 9 0 0 379.495 NA
## elapsed
## 16 21.248
## 17 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 10 fit.models 6 0 0 83.208
## 14 fit.data.training 7 0 0 282.564
## 5 extract.features 3 0 0 24.453
## 11 fit.models 6 1 1 231.997
## 16 predict.data.new 8 0 0 358.247
## 9 select.features 5 0 0 62.061
## 12 fit.models 6 2 2 261.729
## 1 import.data 1 0 0 9.172
## 15 fit.data.training 7 1 1 347.894
## 13 fit.models 6 3 3 276.543
## 2 inspect.data 2 0 0 20.292
## 8 partition.data.training 4 0 0 60.746
## 6 manage.missing.data 3 1 1 59.576
## 3 scrub.data 2 1 1 23.327
## 4 transform.data 2 2 2 24.356
## 7 cluster.data 3 2 2 60.677
## end elapsed duration
## 10 231.997 148.789 148.789
## 14 347.894 65.330 65.330
## 5 59.576 35.123 35.123
## 11 261.728 29.731 29.731
## 16 379.494 21.248 21.247
## 9 83.208 21.147 21.147
## 12 276.542 14.813 14.813
## 1 20.291 11.119 11.119
## 15 358.247 10.353 10.353
## 13 282.563 6.020 6.020
## 2 23.326 3.034 3.034
## 8 62.060 1.314 1.314
## 6 60.676 1.101 1.100
## 3 24.356 1.029 1.029
## 4 24.452 0.096 0.096
## 7 60.745 0.068 0.068
## [1] "Total Elapsed Time: 379.494 secs"
## label step_major step_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3
## 5 fit.models_0_Max.cor.Y[rcv.1X1.cp.0|] 1 4
## 9 fit.models_0_Low.cor.X 1 8
## 8 fit.models_0_Interact.High.cor.Y 1 7
## 6 fit.models_0_Max.cor.Y.Time.Poly 1 5
## 7 fit.models_0_Max.cor.Y.Time.Lag 1 6
## 3 fit.models_0_Random 1 2
## 2 fit.models_0_MFO 1 1
## 1 fit.models_0_bgn 1 0
## label_minor bgn end elapsed duration
## 4 glmnet 91.766 169.327 77.561 77.561
## 5 rpart 169.327 183.585 14.258 14.258
## 9 glmnet 218.981 231.982 13.001 13.001
## 8 glmnet 206.243 218.980 12.738 12.737
## 6 glmnet 183.586 195.343 11.757 11.757
## 7 glmnet 195.344 206.242 10.899 10.898
## 3 myrandom_classfr 87.427 91.766 4.339 4.339
## 2 myMFO_classfr 84.356 87.427 3.071 3.071
## 1 setup 84.325 84.355 0.031 0.030
## [1] "Total Elapsed Time: 231.982 secs"